Machine Learning and Deep Learning for Plant Disease Classification and Detection
A novel classification scheme is proposed that categorizes all relevant works in the associated classes of plant diseases and shows that object detection accuracy is high with YOLOv5 and the networks ResNet50 and MobileNetv2 have the most optimal trade-off on accuracy and training time.
Abstract
Precision agriculture is a rapidly developing field aimed at addressing current concerns about agricultural sustainability. Machine learning is the cutting edge technology underpinning precision agriculture, enabling the development of advanced disease detection and classification methods. This paper presents a review of the application of machine learning and deep learning techniques in precision agriculture, specifically for detecting and classifying plant diseases. We propose a novel classification scheme that categorizes all relevant works in the associated classes. We separate the studies into two main categories depending on the methodology that they use (i.e., classification or object detection). In addition, we present the available datasets for plant disease detection and classification. Finally, we perform an extensive computational study on five state-of-the-art object detection algorithms on PlantDoc dataset to detect diseases present on the leaves, and eighteen state-of-the-art classification algorithms on PlantDoc dataset to predict whether or not there is a disease in a leaf. Computational results show that object detection accuracy is high with YOLOv5. For the image classification task, the networks ResNet50 and MobileNetv2 have the most optimal trade-off on accuracy and training time.