Review—Unveiling the Power of Deep Learning in Plant Pathology: A Review on Leaf Disease Detection

Abstract

Abstract Plant leaf disease identification is a crucial aspect of modern agriculture to enable early disease detection and prevention. Deep learning approaches have demonstrated amazing results in automating this procedure. This paper presents a comparative analysis of various deep learning methods for plant leaf disease identification, with a focus on convolutional neural networks. The performance of these techniques in terms of accuracy, precision, recall, and F1-score, using diverse datasets containing images of diseased leaves from various plant species was examined. This study highlights the strengths and weaknesses of different deep learning approaches, shedding light on their suitability for different plant disease identification scenarios. Additionally, the impact of transfer learning, data augmentation, and sensor data integration in enhancing disease detection accuracy is discussed. The objective of this analysis is to provide valuable insights for researchers and practitioners seeking to harness the potential of deep learning in the agricultural sector, ultimately contributing to more effective and sustainable crop management practices.