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- W4366374030 abstract "This article implements a new method of classification or detection of the diseased section in apple leaf images. Apple leaf disease segmentation and classification is the main processing phase for ROI extraction in agriculture-based IP (image processing). Generally, in the case of live images, it is also crucial to separate the apple leaf images from the background. A frequent experiment in processing leaf images is the automated separation of apple leaves from the background. In the research work, ALS (Alternaria leaf spot), BS (brown spot), Mosaic, etc., are five normal categories of ALD (apple leaf diseases) that rigorously affect apple production. Though the previous work lacks accuracy and faster detection of apple diseases to confirm the healthy growth of apple production, it implements a DL method based on the CNN classifier for detecting apple leaf diseases. The OAGCNN detection model is proposed, which combines an ant colony optimizer and stochastic gradient descent method with the CNN model. The simulation outcomes define the OAGCNN model performance of 98.5 per cent on the ALDD (apple leaf disease database), with a maximum detection time of 2 seconds. The outcomes validate that the OA-CNN classifier model gives maximum performance for the early-stage detection of ALLD and may execute real-time detection of these diseases with maximum accuracy rate and detection complexity than existing techniques such as Vgg-Icep and multi-label classifier methods." @default.
- W4366374030 created "2023-04-21" @default.
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- W4366374030 date "2023-03-03" @default.
- W4366374030 modified "2023-09-30" @default.
- W4366374030 title "An Optimized Ant Gardient Convolutional Neural Network for Disease Detection in Apple Leaves" @default.
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- W4366374030 doi "https://doi.org/10.1109/inocon57975.2023.10101362" @default.
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