Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network.

Abstract

Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.

Plant phenotyping relevance

柑橘の病害・害虫を画像から認識するCNNモデルを開発し、分類精度とパラメータ効率を評価しているため、植物の病害状態を推定する方法が中心である。

Code and data availability

The paper's citrus pest/disease image dataset is publicly hosted via the authors' mycloud link (Appendix B), and the models/code are publicly available on the authors' GitHub (Appendix C). Both are paper-specific, public, and actionable.