Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks.

Abstract

Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.

Plant phenotyping relevance

柑橘の病害を画像から分類するCNN手法の改良と精度比較が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

Code and data availability

The paper explicitly states that the implementation of the phenotyping/classification models (BridgeNet-19 and benchmark CNNs for citrus pest and disease image classification) is publicly available on the authors' GitHub repository. The image dataset itself is described but no separate public deposit URL is given in a