Dice score and standard deviation of the state-of-the-art and the proposed vegetation index methods [9].
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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For the classification of the plants, a self-learning system is used, which learns to recognize different plants [1–3]. These artificial neural networks are part of the most recent artificial intelligence research and are used for a variety of different applications, such as autonomous driving [4, 5], for airline passenger profiling [6], or information processing on the Internet. The application makes it possible to minimize the use of herbicides or to avoid them completely by a nonchemical destruction. This type of environment friendly agriculture would only be possible with many workers who weed the fields by hand. However, the resulting costs do not allow the production of bio-economic products for the mass market. With an all connected system, the farmer is always up-to-date. For example, a drone could tell the robots which field needs to be processed at this point. Other robots could take sowing and harvesting. As a result of the progressing hardware minimization at Westcoast University [7], these agents could be placed directly in small drones in the near future, and a swarm could handle the fields. In this chapter, a vision of the autonomous farm is described. The basic vision and necessity due to the increasing lack of assistants will be explained in section 1. In section 2, the project of the former co-pilot of the Westcoast University of Applied Sciences is presented as an example for the digitization of a work step on the farm. Section 3 presents various sensors and algorithms that are already used for plant selection. Section 4 describes the use of a convolution neural networks, which corresponds to the mathematical reproduction of a brain, for the information processing of a genetic robot. Section 5 explains the environmental friendly destruction of the robot. In the penultimate section 6, future objectives, such as the swarming intensity of different drones, are described. In the last chapter, the advantages and dangers of the digital revolution are shown.
The German version of the Industry 4.0 is made up of the following components:
A complete network of machines, sensors, devices, people, and products via the Internet.
A virtual image of the real world is created via sensor data, thus building up an extended information system.
Assistance systems help people through visualized interfaces to preprocess and filter the information they have acquired, so that faster decisions can be made. In addition, physical, monotonous, strenuous, and dangerous work should be physically relieved.
An information processing system should make all decisions and autonomously control the entire process. It should take decisions independently. Only in the case of problems or target conflicts, the powers should be transferred to a higher authority [8].
If the concept of Industry 4.0 should be transferred to agriculture, a central learning system will take over all the decisions, processes, and problems of the farm. This means that different daemon units measure the fields via sensors, for example, the ground conditions. The moisture or nutrient content is then sent to a database. This creates a virtual and real-time image of the farm. In addition, global information could be obtained via the Internet. The learning system will process past and up-to-date information in real time to create optimal growth conditions for each plant. The system can continually focus on each individual plant. For example, it could determine the best position for each crop on a field. It is conceivable that different plant species are sown on the same field in order to optimally exploit the soil texture. It could access global weather data to calculate the optimal time of the growth cycle. Automated machines could travel independently to the fields and process them. Drones could monitor everything and determine further information about the growth behavior or the environment of the plants. The plants could be sown to new, more precise patterns, in order to better organize the detection and nutrient distribution. Nutrients could be targeted to individual plants, so that each grows optimally. The system could find through historical records, the connection between soil and plant species, which an employee cannot capture. The customer could receive a biography about the whole life of the plant with all influences from the seed to the fruit. All these networked small self-sufficient systems would take over the full farm automatically and manage it.
Furthermore, with VR glasses like the Hololens of Microsoft, human coworkers could get important information about individual plants.
The system could independently process global economic data and develop new economic sectors through the Internet.
Further, the system could develop its own improved devices to optimize the work. Since this system can work 7 days per week and 24 hours a day without getting exhausted, it can deal much more thoroughly with cultivation than a farmer in mass production. Food production for the current human lifestyle has become increasingly polluting. In conventional cultivation, more harmful herbicides and pesticides are used to increase efficiency. It is true that a very efficient pest control is carried out and thus the yield is increased, but this use of chemical also has some side effects. One of the best-known consequences is the increasing pollution of groundwater and drinking water or the death of bees. These two problems alone could put mankind faced with unprecedented challenges. In addition, the herbicides and pesticides pollute the food itself. Therefore, some farmers are trying to establish a more biologically neutral agriculture. Like the farm is Westhof in northern Germany. This is one of the largest farms for organic products in Germany. Due to its environmentally friendly use of the fields, it is prohibited by law to use herbicides in Germany. For example, the farm employs every season a group of workers to free approximately 170 hectares of carrots from weeds. The weeds must be weeded, because the plants compete for nutrients and thus reduce the yield. The Westhof provides more than 170,000 € per year for weeding. Getting workers for these jobs is becoming more and more difficult due to the growing standard of living. Although there are already technologies that have been automated in agriculture, for example, self-propelled tractors or various attachments, at the present time, weeding can only be done by hand. Especially when it concerns plants like the carrot. For this reason, the West Coast University of Applied Sciences in Germany wants to develop a fully automatic weeding robot. This research project is called high-precision weed recognition in organic farming. The autonomously working robot is supposed to take over the weeding day and night in any weather conditions. The project is already in an advanced phase. This development is an automation of a work step but it can also be included in point 3 of the definition list under assistant systems. With its extensive facets, it also provides an insight into the difficulties of the digitization of a farm. Therefore, this project should be used in the following chapters as an example for Industry 4.0 in farming.
The first step toward autonomous eliminating weed is to find out which type of sensors is the most efficient for classification and detection. It is also important to record when the ideal time for weeding is. The co-worker weeds at a late growth period. At this point, the plants already overlap each other. It is therefore not possible to see the weeds under a plant without mechanical effort. For the workers, this is the ideal time, because the advanced growth allows to differentiate individual plants by different characteristics. The robot weeds at an earlier date when the plant and the weed look very similar. This is shown in Figure 1. While this allows an unrestricted view of the plants, it is more difficult to distinguish between them because of their less pronounced characteristics.
Recorded plants by the sensor. Weed encircled red [
Figure 2 shows the robot. The BoniRob has four freely adjustable arms on which the wheels are suspended. Each wheel is driven by its own electric motor and the arm adjustment allows any axis spacing to be set. In addition, the robot has a mounting slot in the center, in which various measuring instruments can be installed [2].
Bonirob on the field of the Westhof Bio in Germany.
For reproducible results, the sensors were sealed off from ambient light. So the influence of the sun on the measurement results can be prevented. A LED-lighting, consisting of six high power LED’s mounted on a profile, was implemented to illuminate the plants properly. The LED’s have a color temperature of 6000 K, which is set at daylight. The lamps are arranged in a circle at a certain angle, so that they have a fixed focus point on the field. By this arrangement, any shade can be prevented and a homogeneous illumination can be achieved [2].
With this configuration, the robot runs autonomously at a speed of 20 mm/s over the field. This speed allows an uninterrupted recording of the field at one image per second, with an overlap of the respective sensor data of approximately 20%. The area on which the plants grow on the wall has an average width of about 15 cm. In order to determine a suitable sensor for recording the plants, the following were investigated: [2]
Bispectral JAI camera.
This camera has a resolution of 1296 × 966 pixels. In addition, it allows the acquisition of coverage-equivalent and simultaneous images from the color range and near infrared range. Two bispectral cameras were always examined at the same time at different angles [2].
Nikon camera D5300
The Nikon D5300 camera has a resolution of 6000 × 4000 pixels. These images do not have an infrared color channel [2].
Kinect II
The Kinect II offers the possibility to record a color image with the size of 1920 × 1080 pixels. The time of flight technology allows a depth of 512 × 424 pixels with 16 bits. An infrared image of 512 × 424 pixels and 11 bits can be read out via the same sensor. However, the depth image and the color image are not coincident and are recorded over two different perspectives [2].
CamCube 3
The CamCube uses the time of flight technology to create depth images and has a resolution of 204 × 204 pixels. Unlike the Kinect, it is possible to use own lenses. As a result, optimal image sections can be seen. A congruent gray image is displayed next to the depth-image [2].
LMI Gocator 2350
The LMI Gocator 2350 is a laser scanner with a resolution in the z direction of 0.019–0.060 mm and a resolution in the x direction of 0.150–0.300 mm. The field of view (FOV) is 158–365 mm [2].
These sensors were evaluated between 22 July 2015 and 14 August 2015 every day under different weather conditions over 1000 meters of rootwalls. By means of these data, it was possible to determine which sensors, configuration, and which stage of growth are best suited for the classification of root plants and weeds [2].
The uneven background and the low resolution of the CamCube 3 prevent a classification between a plant and dirt. The Kinect II has a similar problem. An 3D image of the Kinect is shown in Figure 3. Although it has a much higher resolution and a RGB camera, this sensor is intended for use in a living room as a game interface and therefore has a small focal length. As a result, over 2/3 of the resolution is lost to unimportant areas adjacent to the root rows. The focal point auf the RGB camera is not suitable and produces blurred images due to the distance to the object [2].
Depth map of the Kinect II [
Figure 4 shows a picture of the LMI Gocator 2350. In the picture, different plants can be seen clearly. But the sensor cannot correctly detect the plants in the lower half of the image. Among other things, this is due to the fine structure of the plants and the measurement resolution of the sensor. In addition, there are difficulties to use the LMI Gocator 2350 for plants that are currently in the bud stage. These plants still have a very small area and are located very close to the ground, which means they get lost in the overall information. If the ground is not flat, then it is almost impossible to filter the small structures [2].
Recording of the field with the LMI Gocator 2350 [
The best results are provided by the JAI camera and the Nikon camera. In contrast to the Nikon camera, the JAI has an infrared channel in addition to the RGB channel. The RGB sensor and the IR sensor use the same optics. Thus, it is not possible to focus both images at the same time. However, with this 4-channel image as described in the next chapter, the plants can be extracted from the background by a vegetation index method. The Nikon camera has a high-resolution color camera. Later, instead of the Nikon camera, a 42 megapixel camera with 7716 × 5364 pixels was used. As described in the following chapter, the project has succeeded in creating a robust and real-time segmentation algorithm for RGB images. The used RGB camera is in contrast to the JAI a standard camera and thus easier to interchange, so the choice came down to RGB image recordings. A detailed evaluation of the sensors, as well as a description of the measurement setup, can be found in the paper [2].
The next step for the classification is to separate the plants from the background. The separation of the plants from the background allows the classifier to examine only the important plant pixels in the image. This approach therefore saves time for the classification of a whole picture. The state-of-the-art method used a bispectral camera. Beside an RGB image, this camera also delivers an image from the near infrared region. Both images are congruent because they are taken through the same lens. The plants can be extracted from the background by simply subtracting the red color channel and the infrared channel and then using a global threshold. The state-of-the-art vegetation index determination method works, because the chlorophyll content of living plants causes a greater increase of reflection in the near infrared spectrum than other objects. The big advantage of this method is that dead materials like stones, sticks, or wilted leaves are filtered out. Hence, the extracted plants can then be easily selected and classified. The harsh environment on a field requires robust sensor systems. The mechanical setup of bispectral cameras is more complex compared to standard RGB-color cameras. Hence, standard RGB cameras are more suitable for organic farming applications. Furthermore, they are cheaper and better available, as a wide range of RGB cameras exist on the market. Hence, not only the robustness is better but also the total system costs decrease. This is an important feature as the acceptance of the organic farmer for such systems are only given if the price is much lower than the costs for manual weeding. Another way to extract the plant from the background is to process the HSV-Color-Space. The proposed workflow is shown in Figure 5. In the first step, the captured RGB image is transformed into the HSV-Color-Space [3].
Workflow of the first alternative vegetation index determination [
The HSV-Color-Space uses the colors with the three vectors H for Hue, S for Saturation and V for Value which stands for the brightness. The exact working method is presented in [9] and the result of the algorithm can be seen in Figure 6 [3].
Result of the segmentation.
For a better quantitative result, the ground truth was manually generated. The plants were colored by hand in multiple images. It should be obvious that this method is a subjective one. With the ground truth, it was possible to create a statistical number of over 250 plants. Afterwards, the dice score [10] was calculated
The set X is the ground truth and the set Y is the mask of the vegetation index method used (state-of-the-art, proposed HSV, or RGB algorithm). It should be obvious that if the set X and Y are identical, the calculated mask and the ground truth are equal, hence DS = 1. The dice score and its standard deviation can be looked in Table 1. As it can be seen in Table 1, the proposed methods are better as the state-of-the-art vegetation index method described in [11].
Methods | Standard deviation | |
---|---|---|
State-of-the-art | 0.812 | 0.063 |
HSV | 0.962 | 0.014 |
RGB | 0.943 | 0.018 |
Dice score and standard deviation of the state-of-the-art and the proposed vegetation index methods [9].
A high-resolution camera (6000 × 4000 pixels) was used in a further series of measurements. Again, all the plants could be cut out with the two presented methods without any problems and with a high level of detail, only the state-of-the-art vegetation index method does not work here, because there is no IR image present. With the higher level of details, the classification task becomes easier as more features are available. This will be a significant advantage of the two proposed methods [9].
A more thorough investigation of the algorithm as well as a mathematical derivation and optimization can be found in [3, 9]. A further RGB algorithm is also presented here.
After filtering the background, the plants need to be classified. Therefore, the images of the extracted plants have to be classified manually to create a learning mask. These can be used to train a classifier. There are different classification algorithms, for example, the state-of-the-art random forest classification procedure [11]. One problem of the random forest classifier is to find and calculate suitable features. Another method which is used in this paper, is a convolution neural network classifier, which has to be trained as well and is used in two different ways as described in the following paragraphs [9].
The first process shall evaluate each pixel individually and specify if it is a carrot plant or weed [9].
The convolution neural network for classifying the pixels manner consists of 11 layers and is using a RGB image of 101 × 101 pixels as input information. It is the same structure as in Figure 7, but the output logic is able to classify three classes.
Example CNN for classifying carrot plants in organic farming [
The convolution neural network consists of:
Input layer, size of 3 × 101 × 101
Convolution layer, size of 16 × 5 × 5 (Leaky rectify)
Convolution layer, size of 32 × 7 × 7 (Leaky rectify)
Pooling layer, size of 2 × 2: 2
Convolution layer, size of 32 × 5 × 5 (Leaky rectify)
Convolution layer, size of 64 × 7 × 7 (Leaky rectify)
Pooling layer, size of 2 × 2: 2
Dense layer, size of 64 (Tanh)
Dense layer, size of 64 (Tanh)
Dense layer, size of 64 (Tanh)
Output layer (Dense layer), size of 3 (Softmax)
The output layer consists of three neurons. As a result, three classes can be classified like carrots, weed, and background [9].
This proposed classifier was trained using a CUDA on a GTX Titan with 6 GB memory. It was used a batch size of 500 images. After a learning period of about 3 weeks and 950 million periods, the convolution neural network has been tested [9].
To evaluate the proposed CNN algorithms a huge image database has been build up. The images in the database were taken with a robot, which is shown in Figure 8. The robot named BoniRob drove with constantly 200 mm/sec over the field and took an image every second. These measurements were repeated daily for several weeks under different weather conditions. The images were taken with the Nikon D5300. Seven hundred plants got evaluated. If the majority of the pixels are identified as a plant, it belongs to this class. The resulting classified images of the plants were compared manually to the input images to examine the error rate which was less than one percent at 700 plants. An error means the classification of a weed as a carrot. A carrot was never classified as a weed. This is important for the farmers [9].
Bonirob on the field of the Westhof Bio in Germany.
Figure 9 represents the weed in red and carrots in green. This image was created pixel wise but only every tenth pixel to save time. The white circle shows that the CNN classified a carrot at a weed plant. However, we classified this manually as only a weed plant, which was wrong. Hence, the network corrected us [9].
First result of the CNN [
A better analysis of the accuracy should be done again via the dice score Eq. (1). [10]. Several convolutional neural networks have been designed to perform the classification task. The exact operation is presented in [9]. To train the CNN, other images were used than for testing it. These were unknown test data. The images got first classified by hand, so that a ground truth can be compared. An original image, a ground truth image, and a classified image by the proposed pixelwise CNN from the same area can be seen in Figure 10.
On top, the original image, bottom left side, the ground truth classified by hand image and on the bottom right side, the classified image by the proposed pixelwise convolution neural network.
The classified image with the pixelwise CNN in Figure 10 is smaller, as the origin and the ground truth image. The three in [9] proposed Convolution Neural Networks (pixelwise-, skelton- and area classification) are compared to each other and the results are shown in Table 2. It should be noted that the calculation of the dice score was based on only the pixels that were not classified as background in the ground truth image and the images of the presented algorithms. This is because in the marginal area of a plant, the threshold value does not always calculated exact the same edge lines and thus a misleading error would be calculated.
Methods | Standard deviation | Number of image | Number of classifications | ||
---|---|---|---|---|---|
Pixelwise | Carrot | 0.986 | 0.020 | 45 | 1,339,190 |
Weed | 0.881 | 0.102 | 45 | 188,869 | |
Skeleton | Carrot | 0.983 | 0.024 | 44 | 164,781 |
Weed | 0.889 | 0.077 | 44 | 25,148 | |
Area | Carrot | 0.996 | 0.005 | 25 | 808,069 |
Weed | 0.968 | 0.043 | 25 | 120,840 |
Dice score and its standard deviation of the three proposed convolution neural networks.
In Table 2, it can be seen that the number of classifications of the carrots and the weeds are different. For example, the weed plants are less often seen in Figure 10 as the carrots. While the carrots are relatively homogenous in size, most weeds are relatively small. Therefore, its number of pixels is less than that of carrots. However, it was made sure that a relatively equal number of small and large weeds were used in learning. This could prevent the convolution neural network from triggering only to the size [9].
It can be seen from the Table 2 that the accuracy for the recognition of a carrot pixel is over 98% for all the presented methods with a standard deviation of approximately 2%. The presented area classifier even reaches an accuracy of 99.6% with a standard deviation of only 0.5%. The weeds, on the other hand, have an accuracy of 88.1% and the skeleton of 89%. This means that around 11% of all weed pixels are classified as carrot plants. In the area classification, the accuracy is over 96% for the weed detection and thus almost as well as for the carrots. The default deviation is only 4.3%. The area classifier shows the best results. A complete statistical evaluation can be considered in [9].
The classification results are then transferred to a destruction unit. This destruction unit should remove the weeds environmentally friendly. How exactly this destruction unit will work is examined. There are many different considerations. The methods must have a number of properties. The most important feature is the environmental compatibility that is required by organic farming. Therefore, the use of herbicides is forbidden. Furthermore, the method must be very robust. This means that it has to work several thousand kilometers of carrot rows without great maintenance. Therefore, for example, a method in which the weed is plucked would be too complicated. Looking at the individual steps of the plucking process, it is noticeable that a variety of sensory and motor skills are required to pull a plant out of the soil and not tear it down. Therefore, mechanically simple approaches result. In the following some methods are listed:
Stamping
While stamping, the plant should be removed by pressing it into the soil. It is not completely eliminated, but the crop has an advantage in the distribution of nutrients.
Hot bolt
Similar to stamping, a hot tip should be used here in order to burn the bicarbonate in the growth center.
Mill
A small milling cutter turns the weed from the ground.
Laser
The plant gets burned by a laser. Problems could be the required power, and the lens system that might be stained in the harsh environment.
Maser
Similar to the laser, bundled microwave beams could be used. The advantage would be that this radiation would also get under the earth. It is questionable whether microwave radiation is accepted in ecological agriculture. In addition, the question arises if all the necessary life is removed from the soil.
Electricity
An induction of current could burn the plant, or cause a chemical process in the plant, so that it enters. First experiments with a Tesla coil have been initiated. Targeting of the Tesla beam could be by means of ionized air.
Matter beam
A water cutter/sandblaster could destroy the weed. A problem would be to carry the materials.
Another important feature of the destruction is the speed. In addition, as explained in the next section, further miniaturization is needed. Therefore, it would be desirable if the destruction unit also has a small space and energy requirement.
One of the most difficult problems in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user remains only the state of the art method which is using a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore, a new processor on the basis of a FPGA was developed. The power should be minimized because the use of a FPGA is only a fraction of a GPU application and should therefore be well suited for independent calculations.
The increasing computing power in recent years allows more and more complex algorithms for the implementation of adaptive artificial intelligence. The developed hardware processor consists of three main components: the control unit, the data path and the data manager. The architecture of the proposed hardware processor is illustrated in Figure 11 on page before. The complete configuration of the CPU is shown in [7].
The architecture of the proposed CPU for calculation of convolution neural networks [
The control unit is responsible for determining what operation the processor should just execute. In the program memory, each individual step is deposited. As shown in Figure 11, the core of the control unit is the instruction register (IR). In the IR, the individual steps for each task are written. With each step, a program line is loaded from the program memory [7].
The data manager contains the memory management unit (MMU). It saves the saving address and its information. These are, for example, the starting addresses of the different feature maps [7].
The data path (see Figure 11). The accumulator (AKKU) contains all the data that has to be processed. The data manager knows where to find the information in the AKKU. The arithmetic logic unit (ALU) is responsible for the calculations. Figure 11 shows the multiply and accumulation (MAC)-, the threshold-, and the subsampling-unit. The MAC unit is used for example for the convolution and dense layer [7].
In the future, the CPU will be further developed so that it can be used for plant recognition. The big problem now is that the used FPGA has too little memory to process large networks as needed for plant classification. Therefore, a more suitable FPGA should be used [7].
In paper [7], there is more in depth information about the developed CPU.
In upcoming projects, the existing robot will be optimized by a more cost-effective and more efficient design. Through miniaturization in industry and research, a large number of new technologies have been developed over the past decades. As mentioned in the beginning, it is always difficult to find coworkers for weeding in the field because of the substandard working conditions. Several workers are pulled by a tractor in a reck across the field and weed, the weeds laying faced down to the field. In order to protect the employees from harmful work, a transport device operated by electric motors and a solar panel on the roof was developed. This drives the workers without pollutants and noiselessly over the field.
The robot presented here was the first step toward the automation of agriculture. However, the current BoniRob in its construction with a weight of about one ton and the size of a passenger car is very bulky. After a long trial phase in the field, several disadvantages have arisen. Due to its dimensions, the transport is made more difficult and maneuvering in bad weather conditions is not always possible due to the soil condition. Another disadvantage is the high price of approximately 240.000 €.
To optimize these criteria, the Bonirob will be miniaturized in the future. First, the principle of the electric rack should be transferred to the BoniRob. An extermination unit is installed at the position of the coworker. A central arithmetic system classifies the recorded images and passes the position of the weeds to the destruction unit. Several batteries and a solar panel supply these units. A system allows to run autonomously over the field.
A possible following miniaturization in the future is the use of a drone swarm. The first experiments can be seen on Figure 12. Cameras at the bottom of the drones take pictures of plants. In order to save energy, the images and the GPS coordinates of the images are sent wirelessly to a central server, where the energy- and performance-intensive classification and detection are performed. Subsequently, the data is sent back to the drone and the weed will be destroyed by an extermination unit.
First drones experiments on the field of the Westhof Bio in Germany.
By the complete networking of the sensors in the Industry 4.0, the robots would receive, for example, weather data and decide when the best time to process the fields and when they have to fly back to their station to protect themselves or recharge the batteries.
Through the complete automation, human workers would not be necessary anymore. It is possible that knowledge gets lost. It seems not to be required that someone still needs the extensive knowledge of a farmer. Yet no one could know how the weeds differ from the crop as this is done by the machines. If, for example, a solar storm happens and destroys all electronics, we would be sent back to the Stone Age. At the moment, we did not have the knowledge needed to manage fields. This example applies, of course, to any other branch of industry where artificial intelligence takes over. Therefore, it is imperative to pass on this knowledge, despite the seemingly nonexistent necessity. Again, the autonomous systems could help us. Through special interfaces like the hololence, the system could inform us about its fieldwork experience and explain new insights. Humanity would also be involved in the future and ready to take over the work. Another disadvantage is the high capital needed to invest in automation. The complete connected automation system mentioned in this chapter would cost nowadays millions of euros. With the increasing amount of connected systems and their communication, manipulations and data safety are other aspects to consider.
The robot is only the first step in the development of a fully agricultural farm. Since it is very large and expensive, drones or small beetle-like robots are already considered in future visions. These small autonomous systems could support each other by means of swarm intelligence and process the fields. Not only weeding should be considered, but also the targeted removal of pests. This would enable a completely pesticide-free and herbicide-free agriculture. The robot presented makes it possible to distinguish the plants in small stages of growth with a higher precision than humans can. He also processes the first plant with the same concentration as the last plant. This project represents a step toward the mass-efficient organic agriculture in which the environment is spared and more natural food can be produced. Through these robots and the arrival of industry 4.0 in agriculture, production prices could fall and high-quality foodstuffs could be produced for the whole world.
This work was supported by the Federal State of Schleswig-Holstein, Germany. The authors are grateful for the financial support.
F. J. Knoll. Coast University of Applied Sciences (FHW), Heide, Germany, in 2009. After attending a physic study at Uni Hamburg, he made his M.A. degree at the Westcoast University of Applied Sciences (FHW), Heide, Germany. He is currently working on his doctoral thesis. His research areas include Convolution Neural Networks, real-time 2D/3D image processing, embedded systems design, and algorithm design for FPGA implementation.
Vitali Czymmek received the M.Sc. degree from the West Coast University of Applied Science, Heide, Germany, in 2017. He joined the project High-Precision Weed Recognition as a working student in 2015 and works now as a research associate thesis. His main areas of research interest are digital imaging processing, industrial automation solutions, and their control systems.
Coronary artery disease is a major cause of mortality and morbidity not only in the developed countries but also in developing countries. Over the last decade, mortality with this disease has decreased, but still it accounts for approximately one-third of all the deaths in people over the age of 35 years. The American Heart Association reported that nearly 16.5 million people (20 years or more age) had coronary artery disease in 2017, with male predominance of 55% [1].
\nCoronary artery disease is the narrowing or occlusion of the vessel lumen due to arterial wall thickening caused by subintimal deposition of atheroma and loss of elasticity of the arterial wall. Atherosclerosis involves the proximal portions of the coronary arteries, specifically at the branching sites. In the beginning it only affects the flow reserve of the coronary artery, but as it advances, it affects the blood flow even at rest and leads to myocardial ischemia or infarction depending on the severity of the disease. This can be divided as supply ischemia (myocardial infarction and unstable angina) and demand ischemia (stable angina as during exercise, fever, emotional stress, etc.) [2]. The subendocardium is most vulnerable to myocardial ischemia due to the limited collateral blood flow. Therefore myocardial necrosis progresses from the subendocardium to the epicardium with continuing ischemia.
\nA 75% cross-sectional area loss (50% diameter) is considered an important but moderate stenosis, while a 90% cross-sectional area loss (67% diameter) is considered severe stenosis (Figure 1) [3].
\nRelationship between loss of cross-sectional area and diameter of the vessel.
There are three methods of treating coronary artery disease—medical management, percutaneous intervention, and coronary artery bypass graft surgery [4, 5].
\n\n
Aspirin—it should be used daily in the case of stable angina. Daily use of aspirin in stable angina decreases myocardial infarction and sudden death. Its role is very important in unstable angina and myocardial infarction. Aspirin should also be continued after PCI or CABG.
Platelet inhibitors—it inhibits the binding of the ADP to platelet P2Y12 receptors and consequently inhibits activation of GPIIb/IIIa complex. Therefore it inhibits platelet aggregation. Clopidogrel is recommended if patient is intolerable to aspirin or there are adverse or side effects of aspirin such as GI bleeding or allergic reaction [6]. Both aspirin and clopidogrel are recommended for at least 1 year after PCI and CABG. Aspirin and clopidogrel combination is not recommended for stable angina as it is not superior to aspirin alone and increases the risk of bleeding [7].
Beta blockers—these medicines decrease the myocardial oxygen demand and improve exercise capacity. These are effective for stable angina, and dose should be adjusted to keep heart rate about 60/min at rest and less than 100/min with exercise.
Calcium channel blocker—as beta blockers, these agents are also effective to treat stable angina. They act mainly by causing vasodilatation and reducing peripheral vascular resistance. The dihydropyridines (nifedipine, amlodipine, etc.) do not affect the SA or AV node conduction. Their mechanism of action is by dilating the coronary arteries and reducing the peripheral residence, thereby leading to the decrease in myocardial oxygen demand. On the other hand, non-dihydropyridines (verapamil, diltiazem, etc.) also affect the SA and AV nodes and decrease the oxygen demand.
Nitrates—these are the coronary vasodilator agents. In the lower doses, they are venodilator and reduce the preload, while in higher doses they are also arterial dilators and thereby decrease the afterload too.
Ranolazine—this is the selective inhibitor of the late influx of sodium, thus decreasing the myocardial contractility. It is usually used in combination with beta blockers to treat angina.
Statins—these are hypolipidemic drugs that inhibit the HMG-CoA reductase enzyme and decrease atherosclerotic effect.
ACE inhibitors—they inhibit the angiotensin-converting enzyme. These are the class I recommendation for patients with chronic coronary artery disease with low LVEF (˂40%) or diabetic and a class II recommendation for patients without these mentioned features [8].
Although the term percutaneous coronary intervention refers to any therapeutic coronary artery intervention, it has become synonymous with the percutaneous coronary stent implantation. In the earlier days, bare metal stents were used, but over the last decades, drug-eluting stents are the most commonly used stents for PCI. PCI is performed in patients with stable coronary artery disease and also in settings of acute coronary syndrome.
\n\n
Moderate to severe stable angina with evidence of reversible ischemia.
High-risk unstable angina or ST elevation myocardial infarction (STEMI).
Acute STEMI.
Rescue PCI after failed thrombolysis.
Cardiogenic shock after myocardial infarction (MI).
Revascularization after successful resuscitation.
\n
Lack of vascular access.
Active untreatable severe bleeding.
\n
Bleeding disorder.
Severe renal insufficiency unless patient is on hemodialysis or has severe electrolyte abnormality.
Sepsis.
Poor compliance with medicines.
Terminal illness (advanced or metastatic malignancy).
Short life expectancy.
Difficult coronary anatomy.
Failed previous PCI or not amenable to PCI.
Severe cognitive dysfunction or advanced physical limitation.
Other indication for open heart surgery.
\n
Small area of myocardium is at risk.
No objective evidence of ischemia with either noninvasive or invasive testing (e.g., fractional flow reserve).
Less likelihood of technical success.
Left main coronary artery (LM) or multivessel coronary artery disease with a high SYNTAX score (better for CABG).
Insignificant stenosis (˂50% stenosis).
End-stage cirrhosis with portal hypertension resulting in encephalopathy or visceral bleeding.
Coronary artery bypass grafting is a coronary revascularization by surgery. Dr Rene Favaloro performed his first coronary bypass operation in May 1967 with an interposed saphenous vein graft (SVG) and shortly thereafter used aortocoronary bypasses sutured proximally to the ascending aorta. In the words of Dr Denton Cooley, “Although he [Favaloro] was always hesitant to carry the moniker of ‘father’ of coronary artery bypass surgery, he is the surgeon we should credit with introducing coronary bypass surgery into the clinical arena” [9]. It has been shown to be highly effective in the relief of severe angina and under some circumstances has the capability for considerably prolonging useful life. The stenosed segment of the coronary artery is bypassed using an arterial or venous conduit, and by this it reestablishes the blood flow to the distal ischemic myocardial area. Many studies have shown that surgical revascularization is superior to medical and percutaneous interventional management for multivessel CAD. Full workup should be done before taking patient for surgery.
\n\n
Significant LM disease.
Significant proximal left anterior descending (LAD) disease.
Lesions not amenable to PCI.
Diabetes mellitus.
Depressed left ventricular (LV) ejection fraction.
Indications for CABG can be classified as per Table 1.
\nClassification of indications for CABG surgery.
\n
High-grade LM stenosis.
Significant stenosis (>75%) of proximal LAD with double- or triple-vessel disease.
Symptomatic double- or triple-vessel disease.
Disabling angina despite maximal medical therapy.
Poor LV functions.
Post MI angina.
Post NSTEMI with ongoing ischemia that is unresponsive to medical therapy or PCI.
STEMI with inadequate response to all nonsurgical management.
Emergency CABG.
Failed PCI.
There is no absolute contraindication.
\n\n
Asymptomatic patient with low risk of MI or death.
Advanced age.
Co-morbidities (COPD, pulmonary hypertension, etc.)
\n
Preoperative renal insufficiency.
Peripheral vascular disease.
Recent MI.
Recent brain stroke.
Emergency surgery.
Cardiogenic shock.
\n
Internal thoracic artery (internal mammary artery)
Radial artery
Right gastroepiploic artery
Inferior epigastric artery
Ulnar artery
Splenic artery
\n
Greater saphenous vein
Short saphenous vein
Cephalic vein
Drs. Vineberg and Miller were the first to recognize the properties of internal mammary artery (IMA) and used it for myocardial revascularization in 1945 [10]. They found that it is usually spared from atherosclerosis and reasoned that its branches could form collaterals with myocardial arterioles. They injected contrast medium in postmortem specimens demonstrating connections between the implant and the coronary arteries, but few surgeons took their work seriously [11].
\nLIMA originates from subclavian artery just above and behind the sternal end of the clavicle (Figure 2). The artery descends vertically 1 cm lateral to the sternal border, behind the first six costal cartilages. Figure 3 shows the LIMA position regarding pectoralis muscle, sternum, and pleura with endothoracic fascia. LIMA is widely used these days, especially for the anastomosis with LAD. After dividing the sternum, retractor is placed to lift the left sterna edge. The operating table can be elevated and rotated to expose LIMA properly and harvest it. LIMA can be harvested as pedicle graft (along with internal thoracic veins, fat, muscles, and pleura) or skeletonized vessel. Skeletonized LIMA is supposed to preserve the venous drainage of the sternum, and it is often preferred when there is suspicion of sternal healing and wound infection. All small branches of LIMA are clipped. The proximal end of the LIMA kept attached to the subclavian artery, and then after giving heparin, the distal end is ligated and divided. In the same way as LIMA is harvested, RIMA can also be harvested if it is needed for grafting.
\nInternal mammary artery course.
Relationship of LIMA with sternum, thoracic muscles, pleura, and endothoracic fascia.
Radial artery and palmer arch.
The second most commonly used artery is the radial artery (RA). It is usually harvested from nondominant hand (Figure 4). The RA arises from the bifurcation of the brachial artery in the cubital fossa and terminates by forming the deep palmar arch in the hand. The main concern using RA is blood supply to the wrist and hand. Before using the radial artery, we should assess the patency and collateral blood circulation from the ulnar artery. It can be assessed clinically by Allen’s and modified Allen’s tests. This can also be assessed by preoperative arterial ultrasound. The radial artery can be harvested by open conventional method or endoscopically. The radial artery should be flushed and kept in a solution prepared with Ringer lactate (500 ml), sodium nitroprusside (50 mg), and heparinized blood (30 ml).
\nIt is very rarely used as an arterial graft when other conduits are not available. To harvest this artery, the midline incision over the sternum is extended to the upper abdomen, and the abdominal cavity is opened. There are two gastroepiploic arteries (Figure 5): left and right. Both arteries participate in the stomach vascularization and are collateral blood circulation with other blood vessels of the stomach. Harvesting right gastroepiploic artery as conduit does not compromise stomach blood supply. Branches of this artery to the stomach and omentum are ligated and divided. This artery is positioned either anteriorly or posteriorly to the duodenum and stomach, depending on the tension on graft. A circular opening is made in the diaphragm, medial to the inferior vena cava, and the gastroepiploic artery is passed through this opening to anastomose it with RCA or PDA coronary arteries. The anatomy of gastroepiploic arteries is presented in Figure 6.
\nGastroepiploic artery.
Inferior epigastric artery.
The inferior epigastric artery (Figure 6) arises from the medial aspect of the external iliac artery and gives branches to the spermatic cord, pubis, abdominal muscles, and skin. Its harvesting requires additional either low midline, or paramedian, or oblique inguinal approach. The anterior sheath of the rectus muscle is divided, and then the muscle is pushed medially. The artery is harvested along with the accompanying veins as a pedicle graft and kept in the solution as mentioned for radial artery preservation.
\nWhen the length of this artery is not sufficient for an independent graft, it is used as a composite graft with the LIMA as extension graft.
\nOccasionally, when surgeons do not have other choice, they use the ulnar artery as arterial conduit.
\nWhen there is lack of other conduit, the splenic artery can be used for grafting. The same as harvesting gastroepiploic artery, midline sternal incision can be extended over the abdomen, and the abdomen is opened. Then lesser peritoneal sac is opened. The splenic artery (Figure 7) runs along the superior margin of the pancreas. Branches to the pancreas are ligated and divided. Then the splenic artery is ligated and divided at the splenic hilum and passed through an opening made in the diaphragm medial to the inferior vena cava.
\nSplenic artery.
The greater saphenous vein (Figure 8) is the most commonly used conduit for CABG. The greater saphenous vein (GSV) of the lower extremity is the best choice of this type of graft based on:
There are two independent types of low extremity vein system, and removal of superficial one (GSV) does not jeopardize the venous flow from the leg.
Position, diameter, and length of the GSV are in constant pattern which simplifies its harvest.
Greater and short saphenous veins.
Usually a single long segment is harvested. About 12–15 cm segment may be needed for diagonal branch, about 20–24 cm length for OM branches, and 18–22 cm length for RCA and PDA coronary arteries. Poor quality and veins with varicosities should be avoided. Branches can either be ligated or clipped. These should be ligated or clipped; just flush with GSV to avoid the narrowing or diverticulum. Creation of skin flaps should also be avoided.
\nGSV is harvested in two different ways:
Directly by single full incision or through multiple incisions (Figure 9)
Endoscopic vein harvest
Direct continuous and interrupted harvesting method of SVG.
In rare cases when not enough conduits are available, some surgeons have to use short saphenous vein (Figure 8). It can be harvested by positioning the patient in prone or supine position.
\nEven though we have not used the vein as alternative graft, this can be used in the case other grafts need it. The cephalic vein can be harvested from the wrist up to the shoulder. The walls of the vein are thinner than the saphenous veins, and long-term patency is also lesser than saphenous veins [12].
\n\n
Traditional on-pump CABG.
Off-pump CABG.
Minimally invasive direct.
Totally endoscopic CABG.
\n
A median sternotomy is done (Figure 10).
The same time arterial or venous conduit is harvested.
LIMA is harvested.
Full-dose heparin is given.
LIMA is divided distally and a bulldog clamp is placed on the artery.
Distal segment of LIMA on the chest wall is ligated or clipped.
The pericardium is opened and pericardial stay sutures placed.
Purse string sutures are placed on the ascending aorta and right atrial appendage for cannulation.
A purse string suture is placed on the ascending aorta for cardioplegia cannulation.
Aortic and right atrial cannulae are inserted.
Epiaortic ultrasound can be used to find the site devoid of calcification or atherosclerotic plaques, for cannulation [13].
Aorta is cross clamped and antegrade cardioplegia given.
Cold saline or ice slush can be used for topical cooling.
The heart is retracted out of the pericardial cavity, toward the head of the patient.
Usually first distal graft is done to RCA or PDA (Figure 11).
Graft is distended with blood cardioplegic solution, graft lie is checked, and appropriate length is divided.
The heart is then retracted to the right and then OM is selected for grafting.
Distal anastomosis is done to selected OM vessel.
Graft is distended with solution and appropriate length divided.
The same technique is used for diagonal anastomosis.
Then focus is changed to LIMA-LAD anastomosis.
LIMA is cut to appropriate length.
LIMA-LAD anastomosis is created with Polypropylene 7-0 or 8-0 sutures (Figure 12).
Rewarming is started while doing anastomosis.
A pericardial window is made to pass LIMA without tension.
Phrenic nerve should not be injured.
Aortic cross clamp is removed.
Partially occluding clamp is applied on the ascending aorta (Figure 13).
Two to three openings are made in the aorta using aortic punch.
Proximal anastomosis is done between the aorta and grafts with Prolene 6-0 sutures.
Partially occluding clamp is removed.
De-airing of the grafts is done.
Proximal anastomosis can also be done on the arrested heart with single aortic cross clamp [14],
If the heart is in sinus rhythm, then start weaning off CPB.
Graft flow could be checked with flow meter device.
Protamine is given to reverse the effect of heparin.
Decannulation is done and bleeding secured.
Temporary pacing wire is placed on RV.
One mediastinal and one left pleural drains are inserted.
Sternum is closed with wires.
The skin and subcutaneous tissue are closed in layers with vicryl sutures.
Cannulation for on-pump CABG.
SVG-coronary artery anastomosis.
LIMA-LAD anastomosis.
Side-biting clamp on the ascending aorta for proximal anastomosis.
They should avoid patients with unfavorable characteristics:
Cardiomegaly-making exposure of lateral and inferior walls difficult
Small (<1.5 mm diameter), intramyocardial, and diffusely diseased vessels
Hemodynamically unstable patients
Critical left main disease
Recent acute MI
LV EF <35%
Majority of the cases can be performed without complication after getting a good experience in off-pump CABG, but it is better to avoid if multiple unfavorable factors are present in the same patient (cardiomegaly with EF <25% and small vessels).
\n\n
Preoperative hemodynamic instability
Deep myocardial LAD
Moderate to severe mitral regurgitation
\n
Pulmonary hypertension
Diffuse coronary artery disease
Dense myocardial adhesions during reoperative surgery
Enlarged ascending aorta
LM disease with a non-reconstructable RCA system
\n
Preserve normothermia by keeping the operating room warm.
CPB machine and perfusionist should be available in OR.
No need to prime the CPB machine.
Off-pump setup (octopus, starfish, CO2 blower, intracoronary shunts) should also be available (Figure 14).
Maintaining the systolic BP is important during positioning of the heart for grafting.
Inotropic supports and IV fluid is usually required.
Midline sternotomy.
LIMA and other conduits are harvested.
Heparin is given (1–1.5 mg/Kg) to keep ACT of about 300 s.
ACT should be checked every 20 min and added as required.
Coronary arteries should be grafted in the order of increasing cardiac displacement (anterior wall vessels, then inferior wall, and finally lateral wall vessels).
Most of the surgeons usually do distal anastomoses first, but proximal anastomoses can be done before distal anastomoses.
Performing proximal aortic anastomoses last allows precise estimation of graft length, whereas performing them first allows immediate myocardial reperfusion once each distal anastomosis is completed.
Positioning the heart is very important and needs mechanical stabilizers as octopus and starfish.
Do not compromise the exposure, and if it is not possible, then convert to conventional on-pump CABG.
A silastic tape or vascular sling is passed around the selected coronary artery proximal to the site chosen for anastomosis.
Intracoronary shunts are inserted in coronary arteriotomy.
A CO2 blower is used to disperse the blood to create the anastomosis.
Blower is used selectively when needed and at a rate not >5 L/min to prevent endothelial damage.
A temporary pacing wire can be placed for anastomosis to RCA to combat the bradycardia.
Anastomosis is performed in a routine manner with Polypropylene 7-0 or 8-0 sutures for LIMA grafting or venous grafting.
The intima of both the graft and the recipient vessels must be visualized each time the needle is placed through the anastomosis.
Bilateral IMA grafting (typically to the left heart) has been shown to improve survival and intervention-free survival and should be performed whenever contraindications do not exist.
After completion of anastomoses, heparin could be reversed with protamine.
Protamine is given (0.75–1.0 mg/kg) to incompletely reverse the heparin, leaving each patient with an ACT less than 150 s at the conclusion of the case.
The avoidance of CPB should not come at the price of any compromise in anastomotic precision.
OPCABG setup: octopus and starfish.
In some of the situations, CABG should be performed on CPB. These are as follows:
Ischemic arrhythmias unresponsive to heparin and antiarrhythmic medications.
Cardiogenic shock due to acute infarction or severe global ischemia.
Physical conditions that limit rightward displacement of the heart (deep pectus excavatum, previous left pneumonectomy)
Although avoidance of CPB is generally a worthwhile goal, it does not supersede the goals of hemodynamic stability and complete, precise revascularization.
\n\n
Imperfect technique in cardiac displacement (compressing the right atrium or right ventricle or kinking of the right ventricular outflow tract during rightward displacement of the heart).
Application of compression with the coronary stabilizer—achieve and maintain tissue capture with a minimum of downward pressure on the heart. This will optimize both coronary stabilization and hemodynamic stability.
Less commonly, regional myocardial ischemia is a cause of hemodynamic instability.
The most stenotic vessel is always revascularized first because this vessel is normally well collateralized.
The proximal anastomosis of the graft is performed just after the distal anastomosis of the same graft is completed before proceeding to the next distal anastomosis.
Intracoronary shunts are used to prevent ischemia.
The ascending aorta is side clamp only once during the procedure to minimize the aortic trauma.
The systemic pressure is always reduced around 90–100 mmHg before and during the entire side clamping of the ascending aorta.
The anterior and the inferior territories are always grafted before the posterior territory.
The sequential bypass graft is an effective multiple-bypass technique when graft availability is limited [19] and has been reported to allow improved rates of patency in bypass procedures on narrow coronary arteries [20, 21]. Compared with regular end-to-side anastomosis, however, side-to-side anastomosis is a relatively complex procedure.
\nMethods of side-to-side anastomosis include the following:
A diamond configuration, in which the graft axis lies perpendicular to the axis of the target coronary vessel (crossed side-to-side anastomosis) (Figure 15)
A parallel configuration in which the graft and target coronary axes are aligned (parallel side-to-side anastomosis) (Figure 16)
Continuous or interrupted sutures can be used.
The side-to-side anastomoses used in SB are technically difficult compared with regular end-to-side anastomoses.
Interrupted crossed side-to-side anastomosis greatly simplifies this procedure.
Crossed side-to-side anastomosis.
Parallel side-to-side anastomosis.
In an experimental study with animals, Shioi [22] compared various techniques for performing anastomoses, reporting that crossed side-to-side anastomosis enabled a larger opening than did parallel side-to-side anastomosis and that interrupted sutures enabled a larger anastomotic opening than did a continuous suture.
\n\n
Older age
Female gender
Previous CABG
Urgency of operation
Severe LV dysfunction
Left main disease
Increased extent of coronary artery disease
\n
Freedom from angina or heart failure;
Freedom from the need for medication, rehospitalization, and reintervention.
Preservation of exercise capacity.
Most of the patients have a satisfactory quality of life early after CABG, but this gradually begins to decline after about 5 years [34].
In a study by Suma and colleagues [48] of gastroepiploic artery grafts in 685 patients who underwent postoperative angiographic evaluation, patency was 94% within 1 year in all 685 patients, 88% between 1 and 5 years in 102 patients and 83% between 5 and 10 years in 102 patients. Time-related patency at 1 month, 1 year, 5 years, and 10 years was 96, 91, 80, and 62%, respectively. The principal causes of late occlusion were anastomotic stenoses and anastomoses to less critically stenosed coronary arteries.
For the inferior epigastric artery, Gurne and colleagues [49] performed postoperative angiography in 122 patients early (11 ± 5 days) and in 72 patients late (11 ± 6 months) after operation. Early patency was 98%, and late patency was 93%. Of 14 grafts that were occluded or threadlike at the late study, 8 were anastomosed to arteries with a stenosis of 60% or less.
We believe financial barriers should not prevent researchers from publishing their findings. With the need to make scientific research more publicly available and support the benefits of Open Access, more and more institutions and funders are dedicating resources to assist faculty members and researchers cover Open Access Publishing Fees (OAPFs). In addition, IntechOpen provides several further options presented below, all of which are available to researchers, and could secure the financing of your Open Access publication.
",metaTitle:"Waiver Policy",metaDescription:"We feel that financial barriers should never prevent researchers from publishing their research. With the need to make scientific research more publically available and support the benefits of Open Access, more institutions and funders have dedicated funds to assist their faculty members and researchers cover the APCs associated with publishing in Open Access. Below we have outlined several options available to secure financing for your Open Access publication.",metaKeywords:null,canonicalURL:"/page/waiver-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\\n\\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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\\n\\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\\n\\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\\n\\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\\n\\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\\n\\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\\n\\nDownload Waiver Request Form
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
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\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
\n\nDownload Waiver Request Form
\n\nFeel free to contact us at oapf@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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