"Design and Development of Intelligent Robotic System for Precision Agriculture"

Abstract

Precision agriculture has long been thought to be one of the most difficult fields for robotic farming. Many researchers have already created autonomous tractors, but they have failed owing to their incapacity to comprehend the complexity of the actual world. The primary purpose of this study is to demonstrate the design and construction of an intelligent robotic system capable of collecting real-time environmental data such as AE, leaf index, soil moisture, and picture data and transmitting it to a web server for analysis, categorization, and storage. Four machine learning methods are used in the process: classification, localization, object identification, and segmentation. In the Android Studio environment, a mobile application was designed to monitor the images and submit them to the web CAM server. The suggested model has been interfaced with the mobile application that can be used by smart phones to make a rapid and responsible judgment, allowing farmers to detect and prevent future output losses by allowing them to take safeguards ahead of time. We created and tested a small-scale agricultural robot prototype for precision farming. The experimental findings suggest that it can carry out the basic functions.