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Download PDFOpen PDF in browserDisease Detection in Cotton Plants Using Deep LearningEasyChair Preprint 115254 pages•Date: December 14, 2023AbstractThis article suggests utilizing deep learning models to classify cotton leaves from images captured on the field as a means of identifying any potential lessons. The scourge of agricultural pests and diseases looms large, especially in tropical regions where cotton cultivation is widespread. The pernicious menace has the potential to severely impede crop yields and inflict major financial losses on farmers. Effective solutions are needed for these problems; however, initial symptoms can be challenging to differentiate between making it difficult for farmers to correctly identify lesions. To address this issue, researchers have proposed using deep learning methods that allow monitoring of crop health and better management decisionmaking through screening of cotton leaves. The use of automatic classifier CNN will assist with classification based on training samples gathered from two categories resulting in low error rates during training and improved accuracy when classifying new data examined by our simulation results thus far suggest success within implemented networks at minimum overall detriment or deviation among other variations tested so far respectively. Keyphrases: CNN, DeepLearning, InceptionV3, ResNet50, VGG-16 Download PDFOpen PDF in browser |
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