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Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index
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Jun WANG1, Zhenwei WU1, Hai JIANG1, Juan KE1, Ahmed Prodhan FOYEZ2
Journal of Intelligent Agricultural Mechanization | 2025, 6(2) : 97 - 104
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Journal of Intelligent Agricultural Mechanization | 2025, 6(2): 97-104
Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index
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Jun WANG1, Zhenwei WU1, Hai JIANG1, Juan KE1, Ahmed Prodhan FOYEZ2
Affiliations
  • 1.Jiangsu Lizhuo Information Technology Co., Ltd., Changzhou 213000, China
  • 2.Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
  • WANG Jun, E-mail:

About Author:

WANG Jun, E-mail:

Published: 2025-05-15 doi: 10.12398/j.issn.2096-7217.2025.02.009
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In order to ensure national food security and promote sustainable agricultural development, it is crucial to accurately predict regional rice yield by formulating effective food strategies. While advancemnets have been achieved by integrating remote sensing data with machine learning technology for yield prediction, a more in-depth analysis of the machine learning model mechanism and the application of high spatio-temporal resolution data remains necessary. This study utilized Sentinel and MODIS Normalized Difference Vegetation Index (NDVI) data along with county-level yield statistics from 2023 to implement a Random Forest (RF) model for predicting yield from county level to pixel level with case study of Sheyang unmanned farm in Jiangsu Province, and investigated the impact of different features on the model's learning mechanism. The results demonstrated that there was a significant correlation between NDVI data and rice yield during the period of August to October 2023, which is a critical period in the crop phenological cycle. The RF model effectively predicted rice yield at the county level, with a Root Mean Square Error (RMSE) value of 339.5 kg /hm2. Furthermore, spatial distribution mapping of the predicted yields within Sheyang County for 2023 revealed significant heterogeneity, indicating lower yields in marginal regions and higher yields in central areas ranging from 9 000 to 9 300 kg/hm2. This study not only enhances understanding of machine learning application and vegetation data in yield prediction but also provides theoretical support for improving model accuracy and formulating scientific food production strategies.

Random Forest  /  machine learning  /  remote sensing  /  vegetation index  /  yield forecast  /  growth stage
王俊, 吴振伟, 姜海, 柯娟, Ahmed Prodhan FOYEZ. 基于随机森林及遥感植被指数的无人农场水稻产量预测研究. 智能化农业装备学报(中英文), 2025 , 6 (2) : 97 -104 . DOI: 10.12398/j.issn.2096-7217.2025.02.009
Jun WANG, Zhenwei WU, Hai JIANG, Juan KE, Ahmed Prodhan FOYEZ. Rice yield prediction of unmanned farm based on random forest and remote sensing vegetation index[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (2) : 97 -104 . DOI: 10.12398/j.issn.2096-7217.2025.02.009
  • National Natural Science Foundation of China(42071425)
  • Jiangsu Changzhou Zhonglou District Challenge-Response System Project(JBGS2023011)
Year 2025 volume 6 Issue 2
37
10
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Article Info
doi: 10.12398/j.issn.2096-7217.2025.02.009
  • Receive Date:2024-05-19
  • Online Date:2026-11-05
  • Published:2025-05-15
Article Data
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History
  • Received:2024-05-19
  • Revised:2024-10-10
Funding
National Natural Science Foundation of China(42071425)
Jiangsu Changzhou Zhonglou District Challenge-Response System Project(JBGS2023011)
Affiliations
    1.Jiangsu Lizhuo Information Technology Co., Ltd., Changzhou 213000, China
    2.Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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