A Lightweight Low-Power Model for the Detection of Plant Leaf Diseases

To meet the need for food on a worldwide scale, both in terms of quality and quantity, it is essential to protect plants generally against disease. Even while the problem with diseases is well understood, it remains difficult to quickly identify them, especially in regions where the required infrastructure is missing. One potential answer is to employ edge devices for disease diagnostics. As a proof of idea, the Raspberry Pi is utilized to highlight the potential of smaller variants on compute devices with minimal power. Smartphones are one type of gadget. Currently, 83.72% of people use cell phones worldwide. It would be advantageous to use cell phones to serve this purpose; this project proposes a framework to do so. These images depict real-world situations and are updated using a publicly available PlantVillage dataset, additional images from Google Photographs, the inaturalist website, and real-time image capturing. SqueezeNet and EfficientDet-Lite0 are two deep learning models that are employed. EfficientDet-Lite0 is utilized for object detection, such as leaves, while SqueezeNet is utilized for picture classification, such as plant and disease diagnosis. On a Raspberry Pi 4, the model that was trained can get 97.89% accuracy in less than a second during inference. This should demonstrate how cell phones and other low-powered edge devices may be used to solve this issue.