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.
Highlights
Pests and diseases are major causes of huge economic loss in agricultural production
Ali et al [2] presented a citrus disease recognition system in which a ∆E color difference algorithm was adopted to separate the disease affected areas, and a color histogram and texture features were extracted for classification
Dorafshan et al [21] employed four common edge-detection methods and AlexNet to detect cracks in concrete. Their experimental results showed that the deep convolutional neural networks (CNN) model performed much better than other approaches
Summary
Pests and diseases are major causes of huge economic loss in agricultural production. Bashish et al [1] proposed a solution for automatic detection and classification of plant leaf diseases Their method was based on image processing, which used a K-means clustering technique to segment RGB images and used a single-layered artificial neural network model for classification. Deep convolutional neural networks (CNN) are representation-learning models [5] They can receive raw image data as input and automatically discover the useful features for classification and Sensors 2020, 20, 4992; doi:10.3390/s20174992 www.mdpi.com/journal/sensors. The proposed BridgeNet achieved higher classification accuracy than our previous model (Weakly DenseNet [18]), which proves the effectiveness of the new feature reuse method.
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