A Smart Agriculture Friendly Intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital Image Processing with Learning Approach

Abstract

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions.