Siva K. Balasundram

By Hadi Memarian and Siva K. Balasundram

In this study, the trends of water and sediment data collected from three hydrometer stations over the past 25 years of development in the state of Selangor, Peninsular Malaysia, were analyzed using the Mann–Kendall and Pettitt’s tests. Landscape metrics for establishing the relationship between land use changes and trends of hydrological time series were calculated. The hydrologic trends were also studied in terms of rainfall variations and man-made features. Results indicated upward trends in water discharge at the Hulu Langat sub-basin and sediment load at the Semenyih sub-basin. These increasing trends were mainly caused by rapid changes in land use. Upward trends of hydrological series at the Hulu Langat sub-basin matched its rainfall pattern. At the Lui sub-basin, however, trends of hydrological series and variations in rainfall and land use were not statistically significant.

Part of the book: Landscape Ecology

By Amid Heravi, Desa Ahmad, Ibrahim A. Hameed, Redmond Ramin Shamshiri, Siva K. Balasundram and Muhammad Yamin

A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber.

Part of the book: Agricultural Robots

By Seyed Moin-E-Ddin Rezvani, Redmond R. Shamshiri, Ibrahim A. Hameed, Hamid Zare Abyane, Mohsen Godarzi, Davood Momeni and Siva K. Balasundram

A greenhouse is a complex environment in which various biological and non-biological phenomena occur. For simulation and prediction of the climate and plant growth changes in the greenhouse are necessary to provide mathematical models. The dynamic greenhouse climate models are classified in mechanistic and black-box models (ARX). Climatic models are mainly obtained using energy balance or computational fluid dynamics. In the energy balance models, the greenhouse climatic variables are considered uniformity and homogeneity, but in the computational fluid dynamics, the heterogeneity of the greenhouse environment can be shown by 3D simulation. Crop growth simulation models are quantitative tools based on scientific principles and mathematical relationships that can evaluate the different effects of climate, soil, water, and crop management factors on crop growth and development. In this chapter, with a review of the basics of climate models in greenhouses, the results and application of some climate dynamics models based on the energy balance as well as simulations performed with computational fluid dynamics are reviewed. A review of greenhouse growth models and functional–structural plant models (FSPM) was also conducted.

Part of the book: Next-Generation Greenhouses for Food Security

By Redmond R. Shamshiri, Siva K. Balasundram, Abdullah Kaviani Rad, Muhammad Sultan and Ibrahim A. Hameed

Soil salinity and the water crisis are imposing significant challenges to more than 100 countries as dominant factors of agricultural productivity decline. Given the rising trend of climate change and the need to increase agricultural production, it is crucial to execute appropriate management strategies in farmlands to address salinity and water deficiencies. Ground-based soil moisture and salinity sensors, as well as remote sensing technologies in satellites and unmanned aerial vehicles, which can be used for large-scale soil mapping with high accuracy, play a pivotal role in precision agriculture as advantageous soil condition monitoring instruments. Several barriers, such as expensive rates and a lack of systematic networks, may hinder or even adversely impact the progression of agricultural digitalization. As a result, integrating proximal equipment with remote sensing and Internet of things (IoT) capabilities has been shown to be a promising approach to improving soil monitoring reliability and efficiency. This chapter is an attempt to describe the pros and cons of various soil sensors, with the objective of promoting IoT technology in digital agriculture and smart farming.

Part of the book: Digital Agriculture, Methods and Applications

By Seyed Moin-eddin Rezvani, Redmond R. Shamshiri, Jalal Javadi Moghaddam, Siva K. Balasundram and Ibrahim A. Hameed

Agriculture is constantly developing into a progressive sector by benefiting from a variety of high-tech solutions with the ultimate objectives of improving yield and quality, minimizing wastes and inputs, and maximizing the sustainability of the process. For the case of Iran, adaptation of digital agriculture is one of the key economic plans of the government until 2025. For this purpose, the development of infrastructure besides understanding social and cultural impacts on the transformation of traditional agriculture is necessary. This chapter reports the potential of the existing technological advances and the state of the current research efforts for the implementation of digital agriculture in open-field and closed-field crop production systems in Iran. The focus of the study was on the development of affordable IoT devices and their limitations for various farming applications including smart irrigations and crop monitoring, as well as an outlook for the use of robotics and drone technology by local farmers in Iran.

Part of the book: Digital Agriculture, Methods and Applications