YOLOv5 Crop Detection Deep Learning Model using Artificial Intelligence (AI) and Edge Computing

Abstract

A rising number of firms are working on robotics advancements to create drones, autonomous tractors, robotic harvesters, automated irrigation, and seeding robots. According to papers and research, this problem can be solved utilizing machine learning and deep learning approaches. While some articles claim that employing the correct cameras can improve model accuracy, this is highly reliant on crop and geographical conditions such as sunshine and terrain. This paper suggests a comprehensive method to use edge computing and deep learning to perform binary classification on crops. The model's recall climbed to 99 percent when compared to previous findings, when the recall did not exceed 92 percent. Edge computing and artificial intelligence have the potential to transform agriculture. The usage of Edge computers may greatly cut time, cost, and labour, hence increasing output indirectly. The application developed proved to be useful in improving the model.