Unverified paper record
AI-Driven Advanced Solutions for Plant Leaf Disease Detection and Remediation
2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) · 21 Dec 2023 · 10.1109/icimia60377.2023.10426539
Abstract
In India, agriculture serves as the primary income source for the majority of the population. Identifying crop diseases is a critical factor in mitigating production losses. To address this, deep learning techniques, specifically utilizing pre-trained Convolutional Neural Network (CNN) models such as ResNet-50, VGG-16, MobileNetV2, and InceptionV3, are employed for the detection of plant diseases. This study involves different key stages including dataset creation, preprocessing, data augmentation, and classification. The dataset comprises 3725 images of cotton plant leaves distributed across 11 classes. Here, the model performance is assessed based on classification accuracy, with ResNet-50 achieving the highest accuracy at 99.8% among the four approaches.
Plant phenotyping relevance
植物葉の病徴を画像から分類する深層学習手法の比較・評価が研究の中心であり、植物の病害状態を直接推定するため、方法論文として採用する。
abstractdeep learning techniques, specifically utilizing pre-trained Convolutional Neural Network (CNN) models such as ResNet-50, VGG-16, MobileNetV2, and InceptionV3, are employed for the detection of plant diseases.
abstractHere, the model performance is assessed based on classification accuracy, with ResNet-50 achieving the highest accuracy at 99.8% among the four approaches.
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