2024/12/31 by El Houcine El Fatimi, Fatimi, El Houcine El
Agricultural and Biological Sciences · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Agriculture and AI
paper · pdf · doi:10.48550/arxiv.2501.00669
openalex publication_date 2024/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have proposed a novel method for the detection of various leaf diseases in crops, along with the identification and description of an efficient network architecture that encompasses hyperparameters and optimization methods. The effectiveness of different architectures was compared and evaluated to see the best architecture configuration and to create an effective model that can quickly detect leaf disease. In addition to the work done on pre-trained models, we proposed a new model based on CNN, which provides an efficient method for identifying and detecting plant leaf disease. Furthermore, we evaluated the efficacy of our model and compared the results to those of some pre-trained state-of-the-art architectures.