Organic Farming for Sustainable Agriculture Using Water and Soil Nutrients

Abstract

The agricultural community/farmers are struggling to obtain higher rate of yield due to lack of poor knowledge about the soil and water nutrients and suitability of the organic crop for the soil. Most of the farmers use excessive chemical fertilizers in-order to increase productivity of their yield, without aware of side effects. The excess usage of chemical fertilizers by the farmers will have impact on the quality, fertility, and salinity of the soil. To overcome these issues and to promote Digital Agriculture concept we propose an IoT enabled sensor system for monitoring soil nutrient [NPK] and pH of irrigation water to reduce the manual laboratory method of testing and get the results via mobile application and to promote organic farming in the agricultural field. Smart organic farming based mobile application will further process these nutrients value to predict and suggests the suitable crop to grow and the usage of appropriate amount of fertilizer to maintain the soil fertility there by achieving optimum usage of chemical fertilizer because continuous and wrong usage of these chemical fertilizer have a harmful effect not only on soil but also on crops, we consume leading to unhealthy human life. The proposed mobile application also helps in establishing the connection between farmers and Agricultural Produce Market Committee (APMC) in order to avoid fragmentation of profit shares and attain Pricing uncertainty and marketing of the yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers and suggest the kind of crop to be grown with organic farming. India is well known to produce organic fertilizer which is produced by the waste of slaughterhouses, plant and animal residues, biological products and other natural resources. Thus, the proposed work helps the farmers in adopting stress-free organic farming practice by self-testing their field soil parameters for generating quick soil analysis reports and also helps in connecting with APMC to know the suitable crop for their agriculture land based on the soil and water analysis (SWA) report, dispensing the required amount of organic fertilizer to the soil based on soil and water nutrients analysis using IoT enabled sensor, funding/insurance to the crops in case of occurrence of unpredictable natural disaster in future and direct marketing facility without middle man and maintain sustainable agriculture. In the present era, the industry is at 5.0 levels but agricultural production is still at 2.0 levels. In this chapter a methodology for sustainable agriculture and increase the organic yield of the organic farming using the mobile and IoT technological approaches is presented. A former can obtain the advice and other information for growing the organic crop, organic certification, pricing for the organic yield, selling and other activities by using mobile application in his/her local language. By the proposed work with the ease of mobile application the farmers can perform self-test of their field parameters for generating quick soil and water analysis report, predicts and suggest the suitable organic crop, obtaining the suitable pricing by the APMC and organic certification and agreement to meet the sustainable agriculture. Further the soil fertility of the organic farm can be monitored using IoT enabled sensors which are remotely connected with the mobile application. The experimentation is performed at different agriculture fields with organic farming at six geographical separated villages at Bagalkot district of Karnataka state, India. The different agricultural lands with variety of soil samples is tested to measure the soil parameter such as moisture, temperature, humidity and NPK nutrient values. The pH value of the irrigation water is also determined including borewell, pond, rain, river water etc. available in the reservoirs and promising sustainability in the organic yield is obtained.

1. Introduction

In the era technology enable modern agriculture, the people are keen about the organic farming due to environmental friendliness and more profitable agriculture. The people are diverting towards the organic farming as it uses less chemical-intensive pesticides. At present, agriculture besides farming includes forestry, fruit cultivation, dairy, poultry, mushroom, bee keeping etc. Today marketing, processing and distribution of agricultural products etc. are all accepted as a part of modern agriculture. Thus, agriculture can be defined as the art or science of production of crops and livestock on farm. There are three major types of agricultural practices in India, among them first one is Subsistence Agriculture which means farmers will grow crops for their self needs. Second is Commercial Farming, here farmers will grow crops for earning money by selling the grown crops and the last one is Sustainable Agriculture which is the efficient production of safe, high-quality agricultural product, in a way that protects and improves the natural environment, the social and economic conditions of the farmers.

Sustainable agriculture is farming in sustainable ways meeting sustainability criteria with adaptation of technological approaches. Sustainability criteria can be generally defined as the requirement to the sustainable quality of a product and its sustainable production, which have to be fulfilled in order to acquire a sustainability status. It mainly depends upon three dimensions those are Social Sustainability, Economic Sustainability and Environment Sustainability. The Table 1 depicts the sustainability criteria for attaining the higher yield of the organic crop and sustainable agriculture based on three dimensions.

Earlier days there was no scarcity for food and water as it was used in efficient manner. But now a day there is scarcity of food and water as usage of technology and medicines have been increased due to increase in population growth of the country. To satisfy their needs there is a need in increase in crop production, to produce more crops more amount of fertilizer is being used as it develops more crops in short period of time. Eating these crops grown using more chemical fertilizer will be harmful to the human beings as it causes savior diseases. Hence it is must and should necessary for farmers to know about the usage of fertilizer for cropping and this fertilizer usage depends upon the amount of nutrient present in the soil. The other issue is that farmers do not test their soil and hence they do not know the level of nutrients in soil. Nutrients are more essential for crop’s growth as well as for human health. The deficiency of nutrients content in the crop will cause disease to plant such as stunted growth, leaf yellowing or browning and chlorosis. The human beings eating these nutrient deficient crops are also exposed to severe diseases such as growth disorders, increased susceptibility for infections, skin rashes and immune suppression. These nutrients are classified into micro and macro nutrients. Totally soil consists sixteen nutrients those are carbon, hydrogen, oxygen (which are supplied by air and water), calcium, magnesium, Sulfur (which are secondary nutrients), nitrogen, phosphorous, potassium (which are Macronutrients), boron, chlorine, copper, iron, manganese, molybdenum, and zinc (which are Micronutrients) among them the three major nutrients are Nitrogen (N), Phosphorus (P), Potassium (K).

In organic farming, a proper soil testing will help the farmers to get the correct amount of nutrients present in the soil and to choose the right crop for growing. For achieving the sustainable agriculture, proper management of essential soil nutrients play a vital role. Technology such as IoT and Machine Learning plays an expedient role for improvement of environment and for achieving the economic goals.

Farming is one of the most important occupation since the beginning of civilization. Even though farmer is rich enough to serve the food for every individual of the society but poor himself due to improper pricing for their cultivated crop due to the intervention of the third party in marketing the agriculture products. Since, the farmer’s family is depending on cultivation, if crops are not as per the demands, then farmer’s life becomes miserable hence farming occupation is low admired job. On the other hand, besides of having grown good quality crops or agriculture products they are enable to sell their crops due to occurrence of unexpected global health crisis such as Covid-19 (Novel Coronavirus). Because of the disruptions caused by the Covid-19 outbreak the government has imposed a countrywide lockdown to stop the spread of the coronavirus pandemic. Wherein all internal and external transport has been banned strictly this impacts the farmers with heavy losses due to lack of medium to transport. In such situation adopting to the smart farming techniques is beneficiary, in which everything including buying and selling of agriculture products are done through online sitting in home itself so that it will be profitable to both.

Focusing on encouraging innovation in agriculture, smart farming is the best answer to the problems stated above. This Smart Farming is a concept of agricultural management using modern Information and Communication Technologies such as IoT and Machine Learning to increase the quantity and quality of products. The internet of things (IoT), is a system of interrelated computing devices which are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human interaction. An IoT ecosystem consists of web-enabled smart devices such as sensors to measure the nutrients present in the soil. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is either sent to the cloud to be analyzed or analyzed locally. IoT can also make use of other technology such as machine learning to aid in making prediction based on collected data. IoT can benefit farmers in agriculture by making their job easier.

Machine Learning is the most popular technique of predicting the future or classifying information to help people in making necessary decisions. These algorithms learn from the past instances of data through statistical analysis and pattern matching. Then, based on the learned data, it provides us with the predicted results.

1.1 Overview of proposed work

The organic farming products require proper marketing facilities to the farmers. Information and Communication Technology play a vital role in connecting people all over the world in a fraction of second and has proven impact on their everyday life. It greatly effects in improving the lives of farmers in India. The smart phone is the most commonly used ICT tool all over world, and farmers have started to use mobile phone for communication in their native languages. So, it can help them in direct communication involving the farmers and buyer or customers. By investigating and considering the entire problem faced by farmers the proposed work has introduced smart farming based android mobile application which tests the soil nutrients using the IoT enabled light and color sensors. Farmers need to install the application on their mobile phones so that they can easily get/view the real time soil nutrient [N, P, K] values on their mobile phones. To determine the soil nutrients, value the hardware set up is made which includes sensor, Ardino microcontroller, serial port, cable connector, regulated power supply, soil sample.

The mobile application also helps in establishing the connection between farmers and Agricultural Produce Market Committee (APMC) in order to attain Pricing uncertainty and marketing of the organic farming yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers. APMC admin can view the details of the registered farmers like land area, land number etc. The registered organic farmers of the mobile application get the facility to share the soil and water nutrients reports to the APMC authorities. Further based on the reports, the APMC authorities can recommend the suitable crop to be grown to the farmers and issue the organic certification. And also offers an agreement proposal to the farmer which consists of purchasing price and insurance for their yields thus promoting direct market and distribution facility to the farmers. The organic farming product are costly as it requires more workers and is produced in smaller amounts. According to this agreement, if in situations like Droughts, flood and any other Natural Disasters occurs the APMC authority should pay a minimum amount to the farmers which is fixed by the government. The mobile application also provides the facility for the farmers to get the awareness and usage of new tools and technology, and suggestive measure for the organic farming.

1.2 Scope and objective

The objective of the proposed mobile application used for the organic farming is to,

• Assist the farmers to communicate with APMC for buying and selling of their organic products through online by establishing a link between them.

• Support regional language, hence it increases the usability of mobile application among the farmers.

• Automate the processes of testing the agriculture land to determine the pH of irrigation water and soil nutrients (NPK).

• Facilitate the farmers to share the soil test report with the APMC.

• Suggests the suitable crop based on the uploaded soil test report.

• Facilitate the organic farming with an organic certification, fair pricing for their organic yields without intervention of any middle man through smart farming practices.

1.3 Motivation

The day by day increase in population growth, there is huge need of crop production this crop production mainly depends on the nutrients present in the soil, pH of irrigation water supplied and location specific crop cultivation. Hence it is required for farmers to select suitable crop by monitoring water pH and soil nutrients of the agricultural land. The main motivation of this project is to automate the process of soil nutrient and pH of irrigation water testing and develop an advisory mobile application for farmers to suggests suitable crop based on analysis made on the soil test report to increase crop yield. Farmers usually starts work early during planting and work till harvesting of crops because of their hard work everyone has their daily bread on table. His/her life can be made little better by establishing communication link between APMC authority and farmer to ensure fair marketing price by providing direct selling and buying option through the developed mobile application. Thus, achieving minimum profit out of cultivation. So, farmer’s life can be made a tension free by guarantying buying of the agriculture product before they start cultivation through this mobile app and provide all the necessary information for farming or cultivation hence, this mobile application is developed.

1.4 Organization of chapter

The entire project chapter is organized in six sections. Section 1 Introduces the system, and presents overview, scope and objectives and motivation of the proposed work. Section 2 provides the summary of the existing work and survey of various literatures. Section 3 describes the proposed model with module description, workflow and advantages of system. Section 4 discusses the system design and description of each module of the system. Finally, Section 5 depicts the results and discussion, comparison and method of increasing the accuracy with Graphical result analysis.

1.4.1 Section 2: Literature survey

Organic farming started along with the human civilization but it was totally dependent on nature. Due to increase in the population, there is higher demand for the agricultural products also increased. In-order to cope with this demand farmers started using fertilizers. Indiscriminate use of pesticides and chemicals polluted soil, water and whole ecosystem. Off late our farmers from different parts of India are switching over to sustainable agriculture practice in fields by adopting smart farming techniques as part of Digital India campaign to promote Digital Agriculture.

Exhaustive literature survey is carried out to determine the soil primary nutrients and pH value of the irrigation water of the agriculture field, the technologies employed for the agricultural activities, the mobile applications etc. The research work is enlisted in the Table 2.

Sustainability dimension	Criteria
Social sustainability	Creating self-reliance in organic production Empowering rural communities through partnerships with other farmers to form groups (providing participatory guarantee system and the strengthening of social organization) Increasing farm employment Improving employment opportunities, particularly in rural areas
Economic sustainability	Improving efficiency in areas with low inputs (pesticides, herbicides, etc.) Reducing crop damage and Reducing risk of production Satisfying farmers from an economic perspective Added value of organic products through marketing activities and processing Efficient usage of resources more efficiently (to minimize the use of non-renewable resources) Affordable than traditional agriculture (due to lower variable costs of inputs, identical Fixed costs and higher prices of organic products) Greater economic profitability due to the use of domestic inputs Enhancing the overall performance of the farm in unit area
Environmental sustainability	Maintaining soil fertility in the long-term Improving soil structure Increasing soil water retention potential Producing food without chemicals Using environmentally friendly production methods (such as the use of animal products, intercropping, use of mulch, using natural pesticides, crop residue returned, green manure, compost, crop rotation) Reducing environmental pollution (pollution reduction of water, soil, and air)

Table 1.

Important sustainability criteria in organic farming.

Reference	Proposed work	Sensor used
[1]	IoT sensor for monitoring the soil moisture content with automatic irrigation to save excess water usage.	Soil Moisture Sensors
[2]	Develop an intelligent System to monitor the crop by using drones fitted with a camera eye to record images of crops in a scheduled time	Computer Vision System to monitor the crop
[3]	Monitoring of the field and to provide the proper fertilizers depending upon the soil nutrients.	Soil moisture, pH, PIR, ultrasonic and color sensor
[4]	The soil characters are determined and monitored using various sensors. The data determined are saved in server for later use.	pH sensor, soil moisture sensor, infrared sensor
[5]	Aim is to ease the farmers work by providing automatic planting of crops and monitor the field remotely using ICT enabled tools and technology	Sensor to capture the crop image, and PC to monitor it.
[6]	Soil is foundation for any plant growth. The soil components are measured using various sensors. The real time data are put away and refreshed in cloud worker for further processing	pH sensor, soil moisture sensor, infrared sensor, Humidity sensor
[7]	The proposed of equipment module is constructed utilizing the raspberry-pi as a minicomputer to process all live info information from the sensor to give fundamental data about land information as yield.	Raspberry Pi USB GPS programming.
[8]	Mainly focuses on the strategy to protect the crops during inevitable condition and inculcating technology implementation to promote smart Agro-environment.	GSM and DTMF technology
[9]	Provides systematic agriculture monitoring irrigation techniques which consists of sensor to sense water level of soil and different other parameters of soil.	Soil Moisture Sensor, temperature and humidity sensor.
[10]	Crop productivity mainly depends upon soil and water parameters, the proposed system helps in monitoring the soil and water parameter using several sensors.	Soil Sensor and Infrared sensors
[11]	Objective is to improve the crop production as well as increase the GDP of nation using IoT ecosystem to bring new beginning in the agriculture filed.	Soil moisture, DHT11 sensor and Light sensor
[12]	Goal is to collect live data of agriculture and environment to provide necessary advice on weather condition, crops selection etc. using messages through Short Massaging Service (SMS)	Sensing local agricultural parameters using several soil sensors
[13]	Presents complete automatic drip irrigation system for the agriculture fields by determining the pH, moisture content and the nutrient content of the soil	ARM9 processor, GSM module
[14]	Proposed model aims at restoring the levels of nutrients by monitoring the NPK present in the soil using sensor. And developed a system to provide appropriate amount of fertilizers.	soil fertility sensor, pH sensor and color sensor
[15]	Aims at developing portable handheld device for estimating the nutrient content of the soil and a mobile app for further analysis and comparison.	EC sensor, pH sensor and color sensor

Table 2.

Summary of the literature review.

Location specific cultivation of crop provides better yield and selecting the best suitable crop based on the soil parameter using ICT enabled Machine Learning technology results in high accuracy and sustainable agriculture. Some of recent works done in this direction are summarized and enlisted in Table 3.

Reference	Proposed work	Algorithm used
[16]	Selection of location specific crop plays an important parameter in getting high and stable yield.	Information Fuzzy Network and Data Mining Techniques
[17]	Objective is to predict and suggest the end user/farmer about the crop yield with proper recommendation of fertilizer ratio.	Backpropagation algorithm
[18]	Developed mobile application to support the farmers in getting job notification, search investors across the country and also identify the ripeness of fruit for banana and grapes	Support Vector Machine Classifier
[19]	To increase GDP of nation, a mobile application is developed to predict suitable crop and its future price based on the user input location.	Long Short-Term Memory (LSTM) recurrent neural networks (RNNs)
[20]	developed a mobile application to predict future weather condition of remote area.	Random Forest Classification
[21]	Goal is to build a hardware device to measure soil fertility and accordingly a mobile app is developed to predict and suggest crop and fertilizer plan to farmer.	Support Vector Machine Classifier
[22]	Developed model to classify the soil based on the land type and accordingly predict and suggest the suitable crop to farmers	k-Nearest Neighbor (k-NN), Bagged Trees, Gaussian kernel based Support Vector Machines (SVM).
[23]	Objective of the proposed mobile app is to provide chat forum for farmers to share and get suggestion from experts about crop and fertilizer recommendation.	Predictive Analytics
[24]	Objective is to predict the most profitable crop using data analytics techniques	Multiple Linear Regression
[25]	Predicts the crop yield from the available historical data of Tamil Nadu weather condition.	Random Forest

Table 3.

Location specific cultivation of crop.

1.4.2 Summary of the literature survey

In the literature survey, several techniques are proposed by the authors by covering various areas of the agriculture including usage of different technology such as Internet of Things (IoT) technique for determining and monitoring soil nutrients, Drones for automation of crop management and identifying harvesting period of crop. Further Machine Learning calculations, for example, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM) and Artificial Neural Network (ANN) have been utilized for foreseeing the harvest yield. The mobile applications for the educated farmers in monitoring the agriculture activities including suggestions, notifications, APMC marketing, direct marketing etc.

1.5 Problems in the existing system

No proper guidance about the soil nutrients is available to the farmer, organic crop/food requirement in the market, non-support of local language in the application, lack of awareness about the location specific crop cultivation and less profit due to occurrence of third party in marketing of agricultural products [26].

1.6 Issues and challenges

Some of the issue and challenges of the existing techniques proposed by various authors and reported in the literature are identified and are enlisted in the following:

• Lack of Knowledge About the Soil and Water Parameters: Soil and water are the most important factors for any crops to grow having less knowledge about the soil nutrients and pH of irrigation water will reduce the agriculture profit or in sometimes it results in the crop failure.

• Lack of Concern Towards New Technology: The transformation to new innovation is still amazingly poor and the greater part of the farmers are yet uninformed with respect to such progressions.

• Negligence Related Natural Resources: inattention towards natural resources like water, soil etc. will lead to changes in the agriculture productivity and natural assets.

• Over-Dependence on Traditional Crops than Organic Crops: Earlier farmers follow the traditional cropping system but nowadays due to increased use of fertilizer and same cropping system will reduce the nutrients level of the soil. It results in the reduce crop production and produce low quality crops. Hence selection of crops should be based on the soil parameter and type of soil.

• Poor Marketing: Absence of market offices and helpless government guidelines, and so forth., make it practically incomprehensible for farmers particularly little scope farmers to showcase their homestead produce. Improved market offices and great government guidelines can go far in helping helpless farmers market and benefit from their harvests.

Hence in the proposed work design and develop of organic farming based advisory mobile application to test soil nutrients and pH of irrigation water of agricultural lands there by providing suggestive measures to the farmers for the type of organic crops to be grown, usage of the fertilizers in-order to increase the soil fertility and yield of the land.

2. Proposed system

The smart farming for sustainable agriculture using soil and water nutrients is a IoT enabled system exploring the android mobile application. The proposed work tries to overcome the problems of the existing system and to lower the farming cost a smart farming based android mobile application is proposed to help the farmers in monitoring and maintenance of agricultural land to get high yield, crop selection, promoting organic farming to achieve sustainability in agricultural activities is depicted in Figure 1.

Figure 1.

Flow of the processes in proposed model.

2.1 Block diagram

The proposed organic farming based mobile application provide separate login facility is to both Agricultural Produce Market Committee (APMC) admin and farmer. It also helps farmer to communicate with Agricultural Produce Market Committee (APMC) to avail the facilities provided by the APMC such as uploading soil and water analysis report for knowing suitable crop for growing, sell crops, organic certificate and claim crop insurance through mobile application by establishing a communication link between them.

2.2 Module description

The proposed android mobile application provides several services like IoT based soil nutrient and pH of irrigation water testing, promoting smart contract farming service between APMC and farm producers (farmers) by facilitating an interface to communicate with APMC to view the suitable crop to grow based on uploaded soil analysis report, agreement for purchasing the yields, insurance to crops and obtaining the organic certificate. Thus, the proposed smart farming based mobile application provides separate login modules for both APMC admin and Farmer. The mobile users are categorized based on the services namely Farmer Services and APMC Admin Services, facilities under each category are listed below.

2.2.1 Farmers services

In-order to extend the facilities to the farmer who is involved in organic farming, the mobile application is developed which will provide access to the farmer services on selecting user login option as farmer. Further the farmer can perform several activities in the application based on their choice. Each activity is explored in the following.

1. IoT Enabled Sensor for Soil and Water [SWA] Analysis – Soil and water are important factor that need to considered for effective growth of plants, so it is necessary to analyze these two viz. soil and water, for healthy and sustainable farming. Since the testing laboratories are located one per district and consume more time and delayed response from the testing laboratories for the reports. Hence the village farmers face difficulty in getting tested these parameters from the laboratory. So, the proposed IoT based smart farming mobile application helps in measuring the primary soil nutrients [NPK] and pH value of water using IoT enabled sensor implanted at different locations of the agricultural lands and water reservoirs. This automation will save the time and cost of the farmers. The block diagram of IoT enabled Soil NPK and pH of Irrigation Water testing is shown in below Figure 2.

   The prototype of IoT based soil NPK and pH of irrigation water testing makes use of hardware components like IoT enable Microcontroller, Ethernet shield, soil moisture, temperature and humidity sensor, Color Sensor, Water pH sensor and power supply.

2. Communication with APMC – This module helps farmers to register with APMC to access the facilities provided by the APMC authority. Services provided by APMC to the formers are depicted in the following.

   1. About APMC – This module provides information about the Agricultural Produce Market Committee (APMC).

   2. Upload SWA Report – It provides a facility for farmer to upload the soil and water analysis report of the agriculture land to the APMC admin for knowing the best and suitable crop for growing to get more yield.

   3. View Crop Suggestion and Get Organic Certificate – Admin will suggest the best and profitable crop to the farmers based on their uploaded SWA reports. Admin also offers an agreement clause and Organic certificate to the farmers which provides fair marketing for the farmers yield and also provide insurance facility for the crop.

   4. Signing of Agreement – It provides an option for the farmers to sign the agreement with the APMC. According to this agreement, the yield of the farmer was purchased by APMC at fixed price as mentioned in the agreement provided, if farmers agrees to grow crops that are suggested by APMC admin. The agreement clause also includes the Insurance for the crops in case yield loss due to occurrence of natural disaster like flood, droughts etc. Farmers will get the minimum amount per acre as mentioned in the agreement.

   5. Sell Crops – It gives an internet showcasing office/market to the farmer to offer their yields to the APMC at fixed cost.

Figure 2.

Block diagram of soil NPK and pH of irrigation water analysis.

2.2.2 APMC admin services

APMC is advertising board set up by state government in India to guarantee reasonable promoting for the farmer’s yield. The services offered by the APMC admin are as follows.

1. View and Approve Farmer – It provides facility for admin to view the details of the farmer such as full name, mobile number and kisanId. If details are correct then admin will approve the registered farmer to provide access permission to use the APMC services.

2. View SWA Reports – This module provides facility for the admin to view the SWA reports uploaded by the farmers for further operation such as predicting and suggesting suitable crop to farmer.

3. Predict Crop - This module will help the admin to predict the best and suitable crop based on SWA reports using Machine Learning Algorithm. And also offers an agreement proposal to the farmers for ensuring the fair marketing to the farmers.

4. Buy Crop –APMC administrator can straightforwardly buy crop from ranchers utilizing this android application, it benefits both APMC administrator and ranchers to encourage advantageous reasonable promoting between them.

5. Insurance – It facilitates the admin to approve or reject the insurance claimed by the farmer by verifying the criteria.

In this section, the block diagram of the proposed model and description of its process flow is explained briefly. Detailed discussion and data flow diagrams of each module is explained in subsequent chapter. The soil nutrient and pH of irrigation water testing using IoT enabled sensor technology is explained.

3. System design

The process of system design helps in understanding functionality of the proposed system. The microscopic view of the functionalities of each module in the proposed work is described with the help of data flow diagram (DFD). Further to showcase the process, the case studies are considered and explored in the following sections.

The Figure 3 shows the progression of information for login and enlistment movement procedure of the farmer. The farmer/user enters the login details. If the farmer does not have the login details then he/she needs to register to the mobile application. After successful registration of the farmer to the application the login details are saved then username and password is used for login into the application. Homepage of farmers mobile application consists of several modules and logout option is provided to logoff from the application.

Figure 3.

DFD for login and registration activity of farmer.

The Figure 4 shows the data flow diagrams of services present in the farmer homepage. The farmer needs to first choose upload SWA reading module because all other module operations depend upon upload SWA reading module. Then enter all the required details about the soil parameters and click upload button to upload values. Once after executing this module the rest all module operations get activated.

Figure 4.

DFD for applying/requesting process in farmer homepage.

The admin should enter login details. If login details are correct then the admin login successfully. The Figure 5 shows the data flow diagram of all the available services in the APMC admin homepage. The homepage of APMC admin has the logout button to logoff from the application.

Figure 5.

DFD for applying/requesting process in admin homepage.

3.1 Modules dataflow

The design process helps to know how the developed mobile application is working in a particular way. There are basically two modules and each module associated with certain set of services.

3.1.1 Farmer module

The farmer can communicate with APMC officials to send SWA report collected from the IoT sensors, to get crop and organic farming suggestions for sustainable agriculture, direct marketing etc. The module description shows the services offered to the registered farmer of the mobile application and is depicted in the following.

3.1.1.1 Upload SWA readings

The government authorities have facilitated the agriculturist with Tablet PC/handheld devices. In-order utilize these handheld devices efficiently for agricultural activities, the proposed work developed a mobile application with two modules. The farmer module and APMC admin module. After successful login to the application, farmer can upload SWA reports by entering details of the soil and water analysis readings determined using IoT enabled sensor technology as shown in Figure 6 for further processing.

Figure 6.

Data flow for uploading SWA values.

3.1.1.2 View crop prediction and sign the agreement

The APMC admin offer an agreement proposal to the farmer along with suitable crop and fertilizer suggestion based on analysis done on the uploaded readings of soil parameter. Farmer can view the crop and fertilizer suggestion as show in Figure 7 and have option to sign the agreement. This agreement states that the farmer must grow suggested crop and use the recommended fertilizer. It also provides insurance facility to the crops in case of yield loss due to occurrence of droughts and floods. It helps the farmers to grow crops with tension free because the purchasing price of the crop yield is fixed and mentioned in the agreement.

Figure 7.

Data flow for view crop prediction and signing of agreement.

3.1.1.3 Sell crop

The farmer adds the information of the of the harvest which they need to sell, for example, crop name, amount and all out anticipated cost as shown in Figure 8. All these details of crop information are stored in the database so that APMC admin can retrieve and view crop details from the database to place an order for purchasing the yield.

Figure 8.

Data flow for sell crop.

3.1.1.4 Claim insurance

This service is given to farmer who signed the agreement offered by the APMC shop. Here farmer has facility to claim insurance through the mobile app in case of loss of crop yield due to occurrence of any unavoidable climatic condition such as droughts, floods etc. (Figure 9).

Figure 9.

Data flow for claiming insurance.

3.1.2 APMC admin module

Following module description shows the services offered by developed mobile application to the APMC Admin.

3.1.2.1 View and approve farmer details

The APMC admin has facility to view the farmer details such as name, kisanId and contact number of farmers along with status either approved or pending. The newly registered farmers have the status pending indicating that the admin has not yet approved the registered farmer. After verifying all the farmer details then admin has facility to approve the farmer to give access permission for the farmer to the mobile phone. The Figure 10 shows flow diagram for verifying and approve farmer details. Thus, prevents the unauthorized person accessing the mobile app.

Figure 10.

Data flow for view and approve farmer details.

3.1.2.2 Crop prediction

The crop prediction is a process of predicting the suitable crop for location specific crop cultivation and appropriate fertilizer without degrading the quality of the soil for the sustainable agriculture. The five different steps which are followed in this module are shown in Figure 11.

1. Dataset collection

2. Data pre-processing

3. Data splitting into train and test sets

4. Fitting algorithm

5. Testing/Prediction

1. Dataset Collection

   The dataset containing soil parameters are collected which includes moisture, temperature, humidity, soil type, NPK value, crop name and fertilizer name. The IoT enabled sensors data is stored in the matrix form. The grid size of the IoT enabled sensors from different locations of the agricultural land is 200 X 9. There are totally 200 rows and 9 columns. The first 3 columns contain data which were in string format i.e., NPK values and rest 6 columns contain data which are in numerical.

2. Data pre-processing

   The information gathered from the IoT empowered sensors is in non-uniform and need preprocessing. In data pre-processing all the string data is converted into numerical data. In order to perform this, all the string features are converted into dummy variables which indirectly increased the column number, further the data is cleaned to remove null values. The dataset contains some categorical data and some continuous numeric data. This type of unstructured data cause problems to algorithm, hence the pre-processing task is performed standard feature scaling on all of the data to bring them into a common scale.

3. Data Splitting

   The crop prediction module requires the supervised classifier such as support vector machine (SVM). In-order to train the supervised classifier, it is required to segment the accessible dataset into preparing and testing dataset. It is the way towards parting the dataset into preparing and testing information. In the proposed work the dataset is divided into an 80:20 ratio, the algorithm is trained using the training data and tested using test data to find the accuracy.

4. Fitting algorithm

   The training the data file is to be carried out by loading the cpdata.csv file in order to separates features and labels that are done by applying fit module that is SVM (Support Vector Machine) algorithm used for classification, it works with managed learning method. In the event that the dataset comprise with the main highlights and names SVM works better. As SVM is primarily used for the binarization, binary classifier searches the hyper planes with possibility between positive and negative samples. The multi-SVM is used when the there are many classes from which the classification can happen successfully. There are various strategies offered, where a multi-class classifier is worked by blending the different parallel classifier and afterward used to prepare a SVM classifier in the choice tree root hub utilizing soil information.

5. Prediction of Crop and Organic Certification

   The features extracted are used for prediction of crop and fertilizer for sustainable agriculture. The crop prediction is based on SWA (NPK values of Soil at different locations of the land and pH value of the water at different reservoirs) provided by the IoT enabled sensors. The parameters are validated and further the prediction for the crop is carried out. Further the authorities will issue the Organic certification for the suggested crop.

Figure 11.

Data flow for crop prediction.

3.1.2.3 Buy crops

Once the farmer grow the organic product then farmer need to add the information about crop such as crop name, quantity and expected total price of the crop as shown in Figure 12 which is stored in the database. If the direct buyer/APMC admin wish to purchase agriculture product/yield from the farmer they access the crop details from the database using mobile application to place an order for crop buy. The farmers can avoid the involvement of the mediators in the selling of crop and can earn maximum benefit.

Figure 12.

Data flow for buying crop yield.

3.1.2.4 Approve/reject insurance

In case of occurrences of any natural calamities/disaster such as landslides, flood, drought, fires etc. due to which farmers face loss in their organic farming yield. In-order to solve such issues the developed mobile application provides online insurance claiming facility for farmers. Figure 13 shows the data flow diagram for approving/rejecting of the Insurance from the agency. They have to add the crop details to claim the insurance. Once the request to claim the insurance is sent to the APMC shop then the admin will verify the details of the crop with the existent data to check whether the farmer has grown the suggested crop or not. If the provided details are valid then the admin will approve the insurance else, he will reject the insurance.

Figure 13.

Data flow for approve/reject insurance claim.

In this section, the working flow of data is described using data flow diagrams. It also provides information about yield of every element and the procedure itself. Objective of this system design deals with functionality each module in the system and also provides basic understanding of system characteristics.

4. Experimentation and results

The proposed organic farming model can be implemented in two phases, the first phase is building hardware kit using IoT enabled sensor technology to measure the soil macro nutrients [NPK] and pH of irrigation water. The second phase is developing advisory mobile application using Android studio to predict and suggest the suitable crop and appropriate fertilizer for organic farming to achieve sustainable agriculture. The experiment for measuring the soil parameter and pH of irrigation water has been conducted in fields for different soil samples. The results obtained from the sensor forms repository for further processing to predict crop and fertilizer using machine learning algorithm viz. SVM technique.

The Figure 14 shows the experimental setup of hardware kit which consists of Arduino UNO microcontroller to control all the operation. The IoT enabled sensors namely soil moisture sensor to measure the moisture content of the soil, temperature and humidity sensor (DHT11) to measure the temperature and humidity of soil respectively, pH sensor for water pH value and color sensor is used to determine the soil NPK value.

Figure 14.

Experimental setup of IoT enabled kit.

Experimentation is carried out for six different parts of the Bagalkot district namely Hunugund, Kaladagi, Kerur, Bilagi, Kudalsangam and Mudhol of Karnataka State, India. The different lands of soil samples to measure the soil parameter such as moisture, temperature, humidity and NPK nutrient value of soil. The pH value of the irrigation water is also determined at different locations of land including borewell water, rain water, river water etc. available in the reservoirs. The suitable pH value for the irrigation water for sustainable agriculture should lie between the value 5 to 7.

Experimentation is conducted as shown in Figure 15 to measures the moisture, temperature and humidity content of soil in agriculture field which has grown green grams. The results are uploaded onto the thingsspeak software. This software helps to visualize and analyze live data by plotting graphs, the graph for moisture is shown in Figure 16 and graph for temperature and humidity are as shown in Figure 17.

Figure 15.

Experimental setup of hardware kit to collect NPK values.

Figure 16.

Graph showing soil moisture.

Figure 17.

Graph of humidity and temperature of soil.

Experimentation is additionally done to quantify these essential supplements and results are appeared in Figures 18 and 19.

Figure 18.

LCD display output for soil sample 1.

Figure 19.

Serial monitor output for soil sample 2.

The values which are retrieved from the sensor are transferred to Arduino from there to thingsspeak software and eventually on to the app. The result for different soil sample have been listed in the Table 4. With these measurements the sustainability is maintained and increased organic yield is obtained as depicted in the above Table 1.

Soil Sample No	Moisture	Temperature	Humidity	N	P	K
1	56	22	70	37	45	34
2	58	23	65	125	77	29
3	45	29	49	35	22	24
4	70	26	55	41	56	45
5	60	24	68	38	34	14

Table 4.

Results of sensor readings for different soil sample.

Experimentation is also conducted to determine pH of irrigation water the LCD display value as shown in Figure 20 shows the result of pH of water.

Figure 20.

LCD display value of pH of irrigation water.

The Second step of implementation in the proposed methodology is developing the mobile application to assist the farmer in selecting location specific crop for cultivation and also provide appropriate fertilizer recommendation to the farmer. The developed mobile app can be used by both APMC admin and farmer as shown in Figure 21 through which it establishes a communication link between them.

Figure 21.

Mobile application - direct marketing.

4.1 Farmer registration and login

The application should be installed in the smart phone and farmer need to register to the application as shown in Figure 22 after successful registration the details will be sent to admin for approval. Once the admin approved the farmer details then the farmer can able to login to application using username and password as shown in Figure 22.

Figure 22.

Snapshot of registration and login page of farmer.

4.2 APMC admin login and home page

Agricultural Produce Market Committee (APMC) is a state government body which ensures safeguard to the farmers from exploitation by large retailers. The admin can login to the application using username and password as shown in Figure 23 after successful login the admin can access the home page.

Figure 23.

Snapshot of admin login and home page.

4.3 View and approve farmer details

The admin has the facility to view the farmer details as shown in Figure 24 the status pending shows that the farmer is newly registered and admin will approve the farmer by just clicking on the checkbox button. The status approved indicates that the farmer is an existing user of the application. Only approved formers can successfully login and access the homepage services as shown in Figure 25.

Figure 24.

Snapshot of view and approve farmer.

Figure 25.

Snapshot of farmer home page.

4.4 Upload readings

In Upload soil and water analysis (SWA) readings the details such as moisture, temperature, humidity and NPK value are filled as shown in Figure 26 after clicking the upload button the details will be uploaded to the database for further processing. The APMC admin can use this information for crop and fertilizer prediction as shown in Figure 27.

Figure 26.

Snapshot of uploading SWA readings.

Figure 27.

Snapshots of organic crop prediction.

4.5 View prediction and sign agreement

The farmer can view the suitable crop and appropriate fertilizer as shown in Figure 27 suggested by the admin. Along with this the admin offers an agreement proposal to the farmer. According to this agreement the farmer has to grow only those crops that are suggested by the admin, the purchasing price of the crop yield is fixed in the agreement before the farmer starts growing it and it also provides crop insurance to the farmer. If the farmer agrees to stated agreement criteria to grow the suggested organic crop then he can sign the agreement as shown in Figure 28 by just clicking I agree checkbox and submit button. The request is sent to admin for future operation.

Figure 28.

Snapshot of view prediction and signing of agreement.

4.6 Selling and buying of organic crops

Whenever the farmer wishes to sell the crop yield, he/she fills the details as shown in Figure 29 the crop details will be stored in the database and when the admin login to app to buy the crops then these stored details will be retrieved and displayed to admin.

Figure 29.

Snapshot of selling and buying of organic crops.

4.7 Insurance

Insurance is a means of protection from financial loss. The agreement provides the crop insurance to the farmer, to avail this facility farmer must have signed the agreement. It is given to the farmer whose yield is lost due to occurrences of unavoidable climatic conditions such as droughts, floods etc. to save the farmers from financial loss. Using this mobile app farmer can claim the insurance by providing the necessary details as shown in the Figure 30. The request is sent to the admin for approval, on verifying the details admin has the facility to approve or reject the insurance claim as shown in Figure 30.

Figure 30.

Snapshot of insurance process.

This section shows the experimental results of complete implementation of the proposed method. The experiment is conducted in agriculture fields to measure real time values of soil parameter such as moisture, temperature, humidity and NPK nutrients value and listed the read value in the Table 3. Further these values are used for predicting suitable crop and appropriate fertilizer. The yield of the organic farm is monitored with the help of Mobile and IoT enabled technology for the duration of one year and tabulated the observations, which indicated the production of the yield is increased and the soil fertility is also improved. The proposed methodology obtained the promising results for sustainable agriculture using soil and water nutrients and farmers get direct marketing to their increased organic products at APMC as per the agreement.

5. Conclusion

The proposed system of an organic farming for sustainable agriculture is an advisory mobile application which helps the farmers to self-test their field parameters for generating quick soil and water analysis report and developed android mobile application predicts and suggest the suitable crop to farmers along with agreement proposal to meet the sustainable agriculture practice. It helps to maintain and improve the soil fertility creating ecologically sustainable environment. Thus, the IoT enabled sensor kit helps the farmers to get the soil testing services at the doorstep. It also provides awareness and usage of tools and technology. Hence overall mobile app is used to automate the agricultural process.

6. Future enhancement

The work can be expanded to implement on large fields and skill-based trainings on usage and monitoring of sensor module given to the farmers which helps them to easy the farming practice. Further the developed organic farming based mobile application can also be expanded to implement the government initiative scheme for “One District One Crop“ functionality to augment crop diversity.