Advanced Plant Disease Management Using Keras-Based Models
DOI:
https://doi.org/10.21015/vtse.v13i2.2088Abstract
Cotton crop diseases severely impact agriculture, causing significant yield losses. Traditional methods often detect issues too late, worsening the problem. To address this, AI and remote sensing technologies offer early disease detection, enabling prompt action. This study uses publicly available image datasets from sources like Kaggle/Plant Village, pre-processed with LabelImg and augmented via Keras. Various deep learning models were evaluated, with EfficientNetB0 achieving 82.35% accuracy and EfficientNetV2L reaching 88.24%. Moderate performers like DenseNet201 and ResNet101V2 scored between 58.82% and 64.71%, while InceptionResNetV2, VGG19, and ResNet50 variants had accuracies below 30%. NASNet models failed entirely with 0% accuracy. The findings highlight the potential of AI-driven image processing for early disease detection, promoting resilient and sustainable agriculture by mitigating crop losses. EfficientNet models, particularly EfficientNetV2L, demonstrate superior performance, making them viable tools for precision agriculture and proactive disease management.
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