关键词: 红外热成像技术, 机器学习, 早期病害, 病害检测

Abstract: Early disease detection of crops as a targeted prevention and control method has become an important topic in smart agriculture. The ability to detect crop diseases at an early stage can significantly reduce the use of crop pesticides, improve crop quality, and reduce economic losses. This paper introduces the mechanism of crop diseases and the principle of infrared thermal imaging as the basis for early crop disease detection. It has been found that infrared thermal imaging technology can effectively detect early crop diseases. Next, the working principles of infrared thermal imaging and machine learning are introduced, and the research status of domestic and foreign machine learning and infrared thermal imaging of imaging technology in the field of disease identification is reviewed. Shortcomings of infrared thermal images are analyzed, and machine learning is proposed as a solution to improve the accuracy of early disease detection. The application of machine learning in processing infrared thermal images and the combination of infrared thermal imaging technology and machine learning for crop disease detection are summarized. Based on the current situation, it is found that machine learning can improve the shortcomings of infrared thermal images, and the advantages of the two technologies can be combined for faster and earlier detection of crop diseases. Finally, by analyzing existing research results, the paper proposes solutions to existing problems and discusses future research trends.

Key words: infrared thermal imaging technology, machine learning, early disease, disease detection