標題: RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies
作者: Chen, Wen-Liang
Lin, Yi-Bing
Ng, Fung-Ling
Liu, Chun-You
Lin, Yun-Wei
生物科技學院
資訊工程學系
智慧科學暨綠能學院
College of Biological Science and Technology
Department of Computer Science
College of Artificial Intelligence
關鍵字: Diseases;Artificial intelligence;Meteorology;Agriculture;Sensors;Lesions;Internet of Things;Artificial intelligence (AI);precision farming;rice blast;soil cultivation;spore germination
公開日期: 1-二月-2020
摘要: Rice blast is one of the most serious plant diseases. Many rice blast management approaches require know-how of experienced farmers or agronomists. Monitoring the farm for disease detection is labor intensive and time consuming. By using the Internet of Things (IoT) and artificial intelligence (AI), we are able to detect plant diseases more efficiently. Existing AI and IoT studies detect plant diseases by images or nonimage hyperspectral data, which require manual operations to obtain the photographs or data for analysis. Also, image detection typically is too late as rice blast may already spread to other plants. Based on an IoT platform for soil cultivation, we develop the RiceTalk project that utilizes nonimage IoT devices to detect rice blast. Unlike the image-based plant disease detection approaches, our agriculture sensors generate nonimage data that can be automatically trained and analyzed by the AI mechanism in real time. The beauty of RiceTalk is that the AI model is treated as an IoT device and is managed like other IoT devices. In this way, our approach significantly reduces the platform management cost to provide real-time training and predictions. We also propose an innovative spore germination mechanism as a new feature extraction model for agriculture. In the current implementation, the accuracy of the RiceTalk prediction on rice blast is 89.4%.
URI: http://dx.doi.org/10.1109/JIOT.2019.2947624
http://hdl.handle.net/11536/154230
ISSN: 2327-4662
DOI: 10.1109/JIOT.2019.2947624
期刊: IEEE INTERNET OF THINGS JOURNAL
Volume: 7
Issue: 2
起始頁: 1001
結束頁: 1010
顯示於類別:期刊論文