Remote Crop Disease Detection Using Deep Learning with IoT

Abstract

Agriculture is such a vital part of our society, and according to the United Nations’ Food and Agricultural Organization (FAO), plant diseases are considered one of the two main causes of decreasing food availability. This paper explores not only the methods and findings of building a CNN disease detection model, but that of building a deployable remote crop disease detection system incorporating IoT technology, a task that has not been published before. By using transfer learning with AlexNet, we were able to predict with 89.8% accuracy tomato plant images into one of the ten pre-defined disease classes. Our proposed system tracks plant health throughout the day by using a microprocessor and a camera to automatically capture images, diagnose the plant, and report results. The system is a proof of concept of a technology that can significantly help increase crop yield, reduce food waste, and automate the tasks of detecting and caring for diseased crops.