Within the last decade and a half, ConvNet models have shown increasingly impressive performances on image classification tasks. The continuous quest for better performing ConvNet models is of great importance in that it provides an avenue for the development of superior models which would serve to improve many much-needed services to humankind in domains such as crop pest and disease detection. This paper proposes a feature extraction ConvNet, called Detection, with aim to improve plant disease recognition. Detection contains four convolutional layers, four batch normalization layers and two max-pooling layers. For reference, its plant disease recognition performance was compared with the feature extraction layers for AlexNet, LeNet-5, ZFNet and VGGNet on four datasets from PlantVillage: bacterial spot disease (bell pepper), late blight disease (tomato), leaf mold disease (tomato) and yellow leaf curl disease (tomato). Prior to ablation testing, it achieved the second-highest overall classification accuracy over all four disease recognition datasets (65.50%) after AlexNet (84.33%). However, ablation tests revealed that the removal of the second convolutional layer from the network resulted in a 24.08% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This surpassed AlexNet’s overall accuracy on all four disease recognition datasets (84.33%) by 5.25%. Also, the removal of the second pooling layer from the network resulted in a 23.33% increase in overall accuracy on all four disease recognition datasets, up from 65.50% accuracy without the ablation. This also surpassed AlexNet’s overall accuracy on all four disease recognition datasets (84.33%) by 4.40%. The results suggest that the proposed feature extraction ConvNet is a performant method for plant disease recognition.
Key words: plant disease recognition, convnets, feature extraction layers, ablation testing
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