Abstract: Agricultural practises are essential to maintaining food security and sustaining our growing global population. on the other hand, plant disease pose a serious threat to crop harvests, causing financial losses and food shortages.
The study begins by collecting high resolution photos of plant leaves, paying particular attention to leaves that show signs of illness. The images then pre-processed to enhance their quality and standardize them for analysis. Various machine learning algorithms including Convolutional Neural Networks (CNNs), are trained on this dataset enabling accurate classification of healthy and diseased plant leaves.
The best things is that by developing web-platform for field deployment of the trained machine learning models. This makes it possible to detect disease easily, which helps farmers manage their crops more intelligently. Furthermore, the detecting system made to be user friendly, offering a simple web platform for users to interact with the detecting system.
Keywords- : Plant disease Detection, Machine learning, Web developement, Image processing, Computer vision, Deep learning, Convolutional neural networks, Training dataset, Python programming, TensorFlow.
Cite:
Gauri Bhosale, Atharva Atpadkar, Rutwik Kakade, Mr. V. U. Deshmukh,"Plant Disease Detection Using Machine Learning", IJIREEICE International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 12, no. 2, 2024, Crossref https://doi.org/: 10.17148/IJIREEICE.2024.12214.
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