Open AccessArticle

Machine Learning-Based Crop Growth Diagnosis System Using Spatiotemporal Relative Analysis of Vegetation Indices via a Quartile-Based Method

Volume 11, Issue 4, Page No 35–45, 2026

Author’s Name: Taichi Ito*EmailORCID, Ken’ichi MinaminoEmailORCID
Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, 0200693, Japan
*whom correspondence should be addressed. E-mail: s236w001@s.iwate-pu.ac.jp

Adv. Sci. Technol. Eng. Syst. J. 11(4), 35–45 (2026); crossref symbol DOI: 10.25046/aj110402

Keywords: Geographic Information System, Machine Learning, Paddy Rice, Remote Sensing, Smart Agriculture, Unmanned Aerial Vehicle

Received: 21 July 2026, Revised: 18 August 2026, Accepted: 22 August 2026, Published Online: 30 August 2026
(This article belongs to the SP21 (Special Issue on Emerging Multidisciplinary Directions in Engineering, Computing, and Applied Sciences 2026) & Section Remote Sensing (RMS))
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Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.

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Special Issue on Emerging Multidisciplinary Directions in Engineering, Computing, and Applied Sciences 2026-27
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