Smart Irrigation and Precision Farming of Paddy Field using Unmanned Ground Vehicle and Internet of Things System

Abstract

Paddy is one of the largest consumed staple foods across the globe, especially in Asian countries. With the population growing larger and the agricultural land shrinking, there is a need to increase the yield of the crop to meet the evergrowing food demand. The yield of paddy largely depends on the irrigation of the paddy field, that is maintaining the optimum water level in the paddy field. The solution to this irrigation problem has been proposed in this paper, by addressing various challenges in implementing the Unmanned Ground Vehicle (UGV) and Internet of Things (IoT) system in paddy cultivation. A UGV which carries the sensors were used to collect the sensor data (water level, rainwater, humidity, temperature, light intensity) from the paddy field, which is controlled by cloudbased solution and by the mobile application-based solution. The data was then processed and used to control the water valves which can again be controlled by using cloud and mobile application. Water level maintained by using the mobile application-based solution, cloud-based solution and by following the traditional method of irrigation was compared and the cloudbased solution was found to be more efficient. Thereby providing a solution which reduces the manpower required for the process of irrigation when compared to the traditional irrigation method, also reducing the water wastage, therefore conserving water.

Deep Learning-Based High-Throughput Phenotyping Of Maize (Zea maysL.) Tasseling From Uas Imagery Across Environments

“…Detecting phenotypic variation in near-real-time and at scale poses challenges without automated approaches, given the time-consuming and potentially error-prone nature of manual methods. Various methods have attempted to address these challenges but often do not meet the levels of throughput required for large plant breeding programs and commercial breeding operations (Lu et al 2017, Shao et al 2021, A & Sangeetha, 2021, Murcia et al 2021).…”