Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing

Rodney Tai-Chu Sheng, Yu-Hsiang Huang, Pin-Cheng Chan, Showkat Ahmad Bhat (), Yi-Chien Wu and Nen-Fu Huang ()
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Rodney Tai-Chu Sheng: Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
Yu-Hsiang Huang: Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, Taiwan
Pin-Cheng Chan: Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, Taiwan
Showkat Ahmad Bhat: College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
Yi-Chien Wu: Taichung District Agricultural Research and Extension Station, Changhua County 515008, Taiwan
Nen-Fu Huang: Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan

Agriculture, 2022, vol. 12, issue 12, 1-23

Abstract: Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening stage. These three main stages can be divided into more detailed stages. However, the transitions between stages are challenging to observe and determine, so experience is required. Thus, rice cultivation is challenging for inexperienced growers, even with the standard of procedure (SOP) provided. Additionally, aging and labor issues have had an impact on agriculture. Furthermore, smart farming has been growing rapidly in recent years and has improved agriculture in many ways. To lower the entry requirements and help novices better understand, we proposed a random forest (RF)-based machine learning (ML) classification model for rice growth stages. The experimental setup installed in the experiment fields consists of an HD smart camera (Speed-dome) to collect the image and video data, along with other internet of things (IoT) devices such as 7-in-1 soil sensors, a weather monitoring station, flow meter, and milometer connected with LoRa base station for numerical data. Then, different image processing techniques such as object detection, object classification, instance segmentation, excess green index (EGI), and modified excess green index (EGI) were used to calculate the paddy height and canopy cover (CC) or green coverage (GC). The proposed ML model uses these values as input. Furthermore, growth-related factors such as height, CC, accumulative temperature, and DAT are used to develop our model. An agronomist has been consulted to label the collected different stages of data. The developed optimal model has achieved an accuracy of 0.98772, and a macro F1-score of 0.98653. Thus, the developed model produces high-performance accuracy and can be employed in real-world scenarios.

Keywords: machine learning; image processing; smart farming; paddy rice growth stages; random forest; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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