Real Time Object Detection of Melon Leaf Disease

Abstract

This study suggests a deep convolutional network model for quick and accurate automatic identification using several films of melon leaf disease. The signs of plant melon infections can differ. Expert plant pathologists may be better at recognising diseases than inexperienced farmers. Farmers could benefit greatly from an autonomous system designed to recognise agricultural illnesses by the appearance of the crop and visual symptoms as a verification mechanism in disease detection. The development of quick and accurate techniques for identifying leaf diseases has taken a lot of effort. With the aid of neural networks and digital image processing techniques, plant leaf disease can be detected. Deep learning has advanced greatly during the past few years. Now, it can retrieve pertinent feature representations within deep learning. It can now extract pertinent feature representations from a big dataset of input photos. With the ability to swiftly and precisely identify agricultural ailments made possible by deep learning, plant protection accuracy will increase, and computer vision applications in precision agriculture will become more widespread.