Combining multicolor fluorescence imaging with multispectral reflectance imaging for rapid citrus Huanglongbing detection based on lightweight convolutional neural network using a handheld device

Citrus Huanglongbing (HLB) has posed a great challenge to the citrus production. Timely removal of HLB infected trees was considered as one of the most effective strategies for citrus orchard management. Therefore, rapid detection of HLB disease is urgently needed. The study was aimed to propose an effective method for HLB disease detection by developing a handheld device to capture multicolor fluorescence and multispectral reflectance images synchronously. Additionally, the deep learning and transfer learning technologies were introduced for citrus HLB disease detection. The results demonstrated that the lightweight convolutional neural network (MobileNetV3) can achieve an overall accuracy of 92.1% with the false negative rate of 12.1% at epochs of 33 by combining multicolor fluorescence with multispectral reflectance images as the input of MobileNetV3 model using the dataset of Navel orange. It implied that structural and physiological information from reflectance images and multicolor fluorescence images relating to photosynthesis and secondary metabolites were valuable for rapid HLB disease detection. The transfer learning method of fine-tuning model obtained a superior transferring ability than that of reuse-model with the overall accuracy of 96.5% for Ponkan. These results demonstrated the feasibility of developed handheld device based on multicolor fluorescence and multispectral reflectance imaging combined with deep learning and transfer learning technologies for high throughput HLB disease detection in different infected statuses and cultivars.