生態農業：農業地景生態監測及復育【土木工程學系/楊明德特聘教授】
| 論文篇名 | 英文：Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing 中文：應用智慧手機影像之機器學習評估穀粒收穫水分含量以獲得最佳收穫時間 |
| 期刊名稱 | Sensors |
| 發表年份,卷數,起迄頁數 | 2021, 21(17), no.5875 |
| 作者 | Yang, Ming-Der(楊明德); Hsu, Yu-Chun(許鈺群)*; Tseng, Wei-Cheng(曾偉誠); Lu, Chian-Yu; Yang, Chin-Ying; Lai, Ming-Hsin; Wu, Dong-Hong |
| DOI | 10.3390/s21175875 |
| 中文摘要 | 穀粒水分含量（GMC）是水稻適宜收穫期的關鍵指標。傳統的測試既費時又費力，因此不能在大範圍內實施，並且不能夠估計未來的變化，以揭示最佳收穫。應用智慧手機拍攝單穗的影像，並使用光譜幾何校正板進行校正。每次獲得86個穗樣品，然後在80°C下乾燥7天以獲得濕GMC，共獲得517個有效樣本，其中80%隨機用於訓練，20%用於測試，以構建基於影像的GMC評估模型。在代表現場 GMC 的 1 平方米區域為一樣本，對 201 個樣本進行 17 次 GMC 調查，從而實現多日 GMC 預測。使用主成分分析選擇了八個顏色指數來構建四個機器學習模型，包括隨機森林、多層感知器、支持向量回歸 (SVR) 和多元線性回歸。 MAE為1.23%的SVR模型最適合小於40%的GMC。本研究使用智慧手機提供即時且具有成本效益的非破壞性 GMC 測量，可以在農地預測收穫日期，並促進農業機械的收割排程。 |
| 英文摘要 | 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. |
| 發表成果與本中心研究主題相關性 | 農地的人工智慧辨識 |
