Deep Learning for Image-Based Plant Disease Detection: A Review

Abstract

Plant diseases can reduce crop productivity and quality, making early and reliable diagnosis important for sustainable agriculture. Recent advances in computer vision and deep learning have enabled automated recognition of visible disease symptoms from plant images. This review examines the development of machine learning and deep learning approaches for image-based plant disease detection, with emphasis on convolutional neural networks, transfer learning, publicly available datasets, evaluation measures, and practical deployment. The review synthesizes findings reported in representative studies and identifies recurring methodological patterns. Published work shows that deep learning can achieve very high classification performance on controlled datasets such as PlantVillage, while performance can decrease when models are applied to field images containing variable illumination, complex backgrounds, different growth stages, and previously unseen conditions. Transfer learning and lightweight architectures provide promising directions for resource-constrained devices, but dataset diversity, external validation, interpretability, and real-world generalization remain important challenges. The review concludes that future systems should combine diverse field datasets, robust augmentation, model explainability, efficient architectures, and standardized evaluation protocols. These directions can support the development of practical smartphone- and edge-based tools for crop disease screening while keeping expert agricultural validation in the decision loop.

Author Affiliations & Contributions