Automated Leaf Disease Detection Using Digital Image Processing and Machine Learning

This paper presents the design, development, and implementation of a solar-powered, multitasking agricultural robot aimed at enhancing efficiency and precision in farming operations through advanced automation. The robot integrates a robust Arduino UNO microcontroller platform with a suite of sophisticated sensors, including ultrasonic, infrared, and soil moisture sensors, to autonomously navigate agricultural fields, detect obstacles, and optimize soil conditions in real-time. The primary focus is on achieving high-precision seed sowing, which is crucial for maximizing crop yield and ensuring uniform plant growth. Equipped with a four-wheel drive system and powered by high-efficiency solar panels, the robot is designed to operate sustainably and cost-effectively in diverse agricultural environments. The ultrasonic sensors provide accurate obstacle detection and navigation, while the infrared sensors ensure precise seed placement. Soil moisture sensors continuously monitor soil conditions, enabling the robot to make dynamic adjustments during the planting process. The power management system, featuring lithium-ion batteries and a charge controller, ensures uninterrupted operation by efficiently harvesting and storing solar energy. Control algorithms developed using the Arduino Integrated Development Environment (IDE) enable the robot to perform complex tasks autonomously. These include navigation and path planning, seed dispensing control, and real-time soil condition monitoring. The robot's performance was rigorously tested in both simulated and real agricultural settings, demonstrating high accuracy in seed placement, efficient navigation, reliable soil monitoring, and effective energy management.