PLANT DISEASE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND TRANSFER LEARNING
DOI:
https://doi.org/10.53555/nkqe2507Keywords:
Plant Disease Detection, CNNs, Transfer Learning, MobileNetV2, PlantVillage datasetAbstract
Global food security and agricultural output are seriously threatened by plant diseases, especially when diagnosis is erroneous or delayed. For large-scale farming, manual crop inspection is laborious, subjective, and unfeasible. This work proposes a Plant Disease Recognition System based on Convolutional Neural Networks (CNNs) and transfer learning to address these issues. The suggested solution makes use of a pretrained MobileNetV2 architecture that has been refined using the publicly accessible PlantVillage dataset, which includes more than 54,000 tagged leaf images from various crop species and disease categories. In order to enhance generalization under various visual situations, the methodology places a strong emphasis on robust image preprocessing, such as scaling, normalization, and data augmentation. Users can contribute leaf photos and get real-time illness predictions by accuracy of about 98%. Confusion matrix analysis shows very little misdiagnosis, mostly in cases of early-stage or visually comparable diseases. The results verify that transfer learning dramatically shortens training times without sacrificing classification performance, which qualifies the system for real-world use. The work offers further extensions toward mobile deployment and real-world field validation while highlighting the promise of CNN-based solutions for early plant disease diagnosis.
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