Comparative Analysis of Pre-trained Deep Neural Networks for Plant Disease Classification

Plant diseases are a common and significant problem for farmers worldwide, leading to reduced productivity and eco-nomic challenges for both farmers and countries. Deep learning methods offer an efficient way to classify plant diseases at an earlier stage, enhancing the quality and quantity of agricultural products. Despite the existence of traditional and computer vision classification approaches, they frequently encounter challenges like time-consuming processes, imbalanced data, and restricted field access. This research evaluates several widely used state-of-the-art deep networks on three datasets: PlantVillage, Taiwan dataset, and Citrus Fruits and Leaves Dataset, covering diseases in apple, tomato, and citrus leaves. The evaluation results demonstrate the effective recognition of disease images by deep networks. Notably, the comparison reveals the superiority of specific networks for each dataset: DenseNet201 for PlantVillage - tomato, MobileNetV3 Large for Taiwan dataset - tomato, MobileNetV2 for PlantVillage - apple, and ResNet101 for Citrus Fruits and Leaves Dataset.