Detection Of Crop Disease Using MobileNet

Abstract

Abstract— Crop diseases hit harvests hard, especially when farmers can't spot them early. We’ve been looking at a new deep learning approach that uses MobileNetV2 and mixes structured images with real field photos to catch problems faster. The data covers four classes: Early Blight, Late Blight, Leaf Mold, and healthy leaves. Preprocessing steps like resizing, normalization, augmentation, and cleaning out duplicates really helped the model hold up better. Their MobileNetV2 version hit 91.53% validation accuracy and dropped the loss to 0.235. The training curves stayed steady, and overfitting stayed low. Compared to regular CNNs, this setup gives solid accuracy without needing heavy computing power, which matters when you want something that works right in the field. Keywords—Crop Disease Detection, MobileNetV2, Deep Learning, Transfer Learning, PlantVillage, PlantDoc, Agriculture AI.

Plant phenotyping relevance

葉の画像から病害状態を推定する深層学習手法の開発・性能評価が中心であり、植物フェノタイピング方法に該当する。

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