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- W4361799053 abstract "For several decades, plants have demonstrated to be an effective assessment of human life in a variety of fields. Plant pathogens are currently wreaking havoc on our farming sector. As an outcome, farmers are expected to make a loss. The precise and rapid diagnosis of plant diseases can aid in the development of an early treatment method, reducing enormous economic pain. Manually detecting plant diseases necessitates specialist knowledge of plant diseases, which is difficult, time consuming, and exhausting. In this research, a profound technique for detecting and classifying plant diseases from leaf images captured at multiple resolutions is provided. The inclusion of deep learning networks on top of basic models for effective feature learning is the main goal of this study. Various leaves image datasets from different countries are used to train a dense deep neural networks architecture. Because there were not enough photos, the gathered image data was augmented. In this experiment, 70,874 data shots were utilized to fit the classifier and 17,856 data images have been used to analyze it. The suggested CNN architecture can accurately categorize miscellaneous varieties of plant leaves, according to experimental evidence, and provide the possible remedies. In terms of accuracy, precision, recall, and F-score, a comparison of MobileNetV1 and ResNet34 structure was conducted, and the results revealed that the ResNet34 model is an efficient approach for disease categorization. The results of the experiments show that on photographs with complicated backgrounds, an average test accuracy of 98.91% may be achieved. The processing time is 0.067 s with considerable precision for an individual plant leaf image, indicating that it is both real time and practicable." @default.
- W4361799053 created "2023-04-05" @default.
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- W4361799053 date "2023-01-01" @default.
- W4361799053 modified "2023-09-27" @default.
- W4361799053 title "Plant Diseases Detection Using Transfer Learning" @default.
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- W4361799053 doi "https://doi.org/10.1007/978-981-19-8563-8_1" @default.
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