Browsing by Author "Ng, Fung-Ling"

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AgriTalk: IoT for Precision Soil Farming of Turmeric Cultivation
(2019-06-01)
Chen, Wen-Liang
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Lin, Yi-Bing
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Lin, Yun-Wei
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Chen, Robert
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Liao, Jyun-Kai
This paper proposes AgriTalk, an inexpensive Internet of Things (IoT) platform for precision farming of soil cultivation. We conduct experiments on turmeric cultivation, which indicates that the turmeric quality is significantly enhanced through AgriTalk. Specifically, the curcumin concentration is up to 4500-5500 mg/100 g, which is five times more than existing products. We demonstrate how to intuitively configure the connections between the sensors and the actuators with the desired farming intelligence, and to effectively maintain AgriTalk for precision farming. We conduct measurement, analytic analysis, and simulation experiments to investigate the IoT message delays of AgriTalk. This paper indicates that the delays for automatic control and automatic-manual control switching with long distances (more than 30 km) are very short (less than 0.2 s) and AgriTalk can easily respond to quick and dynamic change of the field environment conditions in soil cultivation.
RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies
(2020-02-01)
Chen, Wen-Liang
;
Lin, Yi-Bing
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Ng, Fung-Ling
;
Liu, Chun-You
;
Lin, Yun-Wei
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%.