Data_Sheet_1_Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection.docx

Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considering that it costs much time and energy to develop a classifier for each variety of rice, deep transfer learning was firstly introduced to rice disease detection across different rice varieties. Three deep transfer learning methods were adapted for 12 transfer tasks, namely, fine-tuning, deep CORrelation ALignment (CORAL), and deep domain confusion (DDC). A self-designed convolutional neural network (CNN) was set as the basic network of the deep transfer learning methods. Fine-tuning achieved the best transferable performance with an accuracy of over 88% for the test set of the target domain in the majority of transfer tasks. Deep CORAL obtained an accuracy of over 80% in four of all the transfer tasks, which was superior to that of DDC. A multi-task transfer strategy has been explored with good results, indicating the potential of both pair-wise, and multi-task transfers. A saliency map was used for the visualization of the key wavelength range captured by CNN with and without transfer learning. The results indicated that the wavelength range with and without transfer learning was overlapped to some extent. Overall, the results suggested that deep transfer learning methods could perform rice disease detection across different rice varieties. Hyperspectral imaging, in combination with the deep transfer learning method, is a promising possibility for the efficient and cost-saving field detection of rice diseases among different rice varieties.

多种水稻病害均会威胁水稻生长，实现水稻病害的快速精准检测对于开展精准病害防控工作具有重要意义。本研究采用高光谱成像（Hyperspectral Imaging, HSI）技术，对四类不同水稻品种的稻叶病害进行检测。考虑到针对每个水稻品种单独开发分类器需耗费大量时间与精力，本研究首次将深度迁移学习引入跨水稻品种的稻病害检测任务中。针对12项迁移任务，本研究采用了三类深度迁移学习方法，即微调（fine-tuning）、深度相关对齐（deep CORrelation ALignment, CORAL）以及深度域混淆（deep domain confusion, DDC），并将自主设计的卷积神经网络（Convolutional Neural Network, CNN）作为各深度迁移学习方法的基础网络。实验结果显示，在多数迁移任务中，微调方法在目标域测试集上的准确率可达88%以上，迁移性能最优；深度相关对齐在其中4项迁移任务中准确率超过80%，其性能优于深度域混淆方法。本研究还探索了多任务迁移策略并取得了良好效果，表明成对迁移与多任务迁移均具备应用潜力。此外，研究采用显著性图（saliency map）对有无迁移学习场景下卷积神经网络捕获的关键波长范围进行可视化，结果显示两种场景下捕获的波长范围存在一定程度的重叠。总体而言，实验结果证明深度迁移学习方法可有效实现跨水稻品种的稻病害检测。高光谱成像与深度迁移学习方法相结合，为不同水稻品种间稻病害的高效、低成本田间检测提供了极具前景的技术方案。