Generalizing In-Field Plant Disease Diagnosis: A Deep Transfer Learning Approach for Multi-Crop and Heterogeneous Imaging Conditions

Plant diseases pose a serious threat to agricultural productivity, which can cause significant crop losses if not addressed quickly and appropriately. There are significant opportunities for digital image-based treatment with computer vision and artificial intelligence. The main challenges in recognizing image-based plant diseases are: developing a single model capable of diagnosing diseases in various types of plants. Ensuring the model remains reliable even when images are taken under varying lighting conditions, backgrounds, and camera quality. In addition, the challenge in this study is to present a model capable of recognizing leaf diseases of multiple food crops, especially rice and corn. The purpose of this study is to identify leaf diseases of rice and corn crops. This study proposes deep learning and transfer learning for diagnosing plant leaf diseases in various types of plants and unstructured imaging environments. To address these challenges, a selection of VGGNet, ResNet50, InceptionV3, and EfficientNetB0 methods was conducted by testing them using laboratory datasets. Based on the testing, the EfficientNetB0 model performed the best. Then, the selected model parameters were tuned, feature extraction and a new dataset was collected in a real-world domain with varying lighting, changing viewpoints and scales, complex backgrounds, similar symptoms between diseases, and occlusion. The results showed that the proposed model performed very well and robustly, with 98% accuracy and a weighted average F1-score of 98% in identifying food crop diseases: blight, rust, blast, blight, tungro, and healthy leaves. This performance indicates that the developed model is highly reliable in classifying leaf diseases in rice and corn. This model is expected to be applied to precision agriculture technology so that farmers can take timely action regarding treatment without further delay.