Verdant Vision: CNNs Revolutionizing Plant Leaf Disease Identification
DOI:
https://doi.org/10.48001/jocsvl.2024.121-6Keywords:
Automated detection, Convolutional Neural Networks (CNN), Machine Learning (ML), Plant disease detection, VisionAbstract
The advancement of technology has enabled the accurate and efficient detection of plant diseases, demonstrating the use of machine learning, especially Convolutional Neural Networks, which have become widely popular. Using the models of the CNN, it is realistic to create an application that identifies a disease based on photographs of the plants with the help of textures, leaf spots, sheen alterations, and other features. Since Convolutional Neural Networks are trained with large samples of diseased and healthy plant pictures, they are more adaptable to new unseen conditions. Therefore, medical diagnosis is more accurate and faster because of automating disease detection. Consequently, less effort of manual examination is needed. To prevent the spread of the disease and restrict its permanent effects, automated disease detection helps to detect pathogen symptoms in healthy plants during the early developmental stages. It has been successful in implementing all kinds of disease detection methods on many crops and, as such, satisfying the need for precision agriculture and reducing losses of the crops.
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References
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