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

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

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...

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Bibliographic Details
Main Authors: Ming-Der Yang, Yu-Chun Hsu, Wei-Cheng Tseng, Chian-Yu Lu, Chin-Ying Yang, Ming-Hsin Lai, Dong-Hong Wu
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Sensors
Subjects:
machine learning
grain moisture content
smart phone
optimal harvest timing
random forest
support vector regression
Online Access:https://www.mdpi.com/1424-8220/21/17/5875

Internet

https://www.mdpi.com/1424-8220/21/17/5875

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