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Article

Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing

1
Department of Civil Engineering; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 40227, Taiwan
2
Pervasive AI Research (PAIR) Labs, Hsinchu 30010, Taiwan
3
Department of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan
4
Crop Science Division, Taiwan Agricultural Research Institute, Taichung 413008, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Lei Shu
Sensors 2021, 21(17), 5875; https://doi.org/10.3390/s21175875
Received: 17 July 2021 / Revised: 20 August 2021 / Accepted: 25 August 2021 / Published: 31 August 2021
(This article belongs to the Special Issue Sensing Technology in Smart Agriculture)
Grain moisture content (GMC) is a key indicator of the appropriate harvest period of rice. Conventional testing is time-consuming and laborious, thus not to be implemented over vast areas and to enable the estimation of future changes for revealing optimal harvesting. Images of single panicles were shot with smartphones and corrected using a spectral–geometric correction board. In total, 86 panicle samples were obtained each time and then dried at 80 °C for 7 days to acquire the wet-basis GMC. In total, 517 valid samples were obtained, in which 80% was randomly used for training and 20% was used for testing to construct the image-based GMC assessment model. In total, 17 GMC surveys from a total of 201 samples were also performed from an area of 1 m2 representing on-site GMC, which enabled a multi-day GMC prediction. Eight color indices were selected using principal component analysis for building four machine learning models, including random forest, multilayer perceptron, support vector regression (SVR), and multivariate linear regression. The SVR model with a MAE of 1.23% was the most suitable for GMC of less than 40%. This study provides a real-time and cost-effective non-destructive GMC measurement using smartphones that enables on-farm prediction of harvest dates and facilitates the harvesting scheduling of agricultural machinery. View Full-Text
Keywords: machine learning; grain moisture content; smart phone; optimal harvest timing; random forest; support vector regression; feature extraction; smart agriculture machine learning; grain moisture content; smart phone; optimal harvest timing; random forest; support vector regression; feature extraction; smart agriculture
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MDPI and ACS Style

Yang, M.-D.; Hsu, Y.-C.; Tseng, W.-C.; Lu, C.-Y.; Yang, C.-Y.; Lai, M.-H.; Wu, D.-H. Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing. Sensors 2021, 21, 5875. https://doi.org/10.3390/s21175875

AMA Style

Yang M-D, Hsu Y-C, Tseng W-C, Lu C-Y, Yang C-Y, Lai M-H, Wu D-H. Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing. Sensors. 2021; 21(17):5875. https://doi.org/10.3390/s21175875

Chicago/Turabian Style

Yang, Ming-Der, Yu-Chun Hsu, Wei-Cheng Tseng, Chian-Yu Lu, Chin-Ying Yang, Ming-Hsin Lai, and Dong-Hong Wu. 2021. "Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing" Sensors 21, no. 17: 5875. https://doi.org/10.3390/s21175875

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