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Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection
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Zhichang ZHU1, Yan GE1, 2, Jingrong ZANG3, Qing LI3, Shichao JIN4, Huanliang XU1, Zhaoyu ZHAI1
Journal of Triticeae Crops | 2025, 45(2) : 264 - 274
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Journal of Triticeae Crops | 2025, 45(2): 264-274
Physiology, Ecology and Cultivation
Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection
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Zhichang ZHU1, Yan GE1, 2, Jingrong ZANG3, Qing LI3, Shichao JIN4, Huanliang XU1, Zhaoyu ZHAI1
Affiliations
  • 1.College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
  • 2.College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
  • 3.College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
  • 4.Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
Published: 2025-02-15 doi: 10.7606/j.issn.1009-1041.2025.02.14
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Accurate and effective yield prediction is essential for wheat breeding, cultivation and field management. In this study, the multispectral and RGB images of winter wheat during the grain filling stage were collected from UAV, and 14 spectral traits and 28 morphological traits were extracted as feature variables. Ten machine learning methods, including linear regression, random forest and neural network, were used to construct wheat yield prediction models, and the differences between the models were compared to select the best one. Additionally, machine learning interpretability method SHAP was introduced to analyze the importance of the feature variables, in order to improve the prediction performance of the model. The results showed that among the 10 machine learning methods used, the BPNN model had the best prediction performance (r2=0.826, RMSE=0.094 t·hm-2). According to the feature importance ranking determined by SHAP, Anthocyanin Reflectance Index (ARI) and Three-Dimensional Canopy Volume (Volume) had the greatest impact on the prediction results, accounting for 45.48% of the total feature importance. After feature selection using SHAP, the BPNN model with the best performance was determined based on nine feature variables (r2=0.865, RMSE=0.075 t·hm-2). This improved the prediction accuracy compared to the BPNN model using all features and the pre-analysis method Pearson correlation analysis. Therefore, based on the optimal yield prediction model, SHAP mechanism can be used to select and analyze the importance of feature variables, so as to further improve the accuracy of wheat yield prediction.

Wheat  /  UAV imagery  /  Machine learning  /  Shapley additive explanations  /  Yield prediction
Zhichang ZHU, Yan GE, Jingrong ZANG, Qing LI, Shichao JIN, Huanliang XU, Zhaoyu ZHAI. Research on Wheat Yield Prediction Based on UAV Imagery and SHAP Feature Selection[J]. Journal of Triticeae Crops, 2025 , 45 (2) : 264 -274 . DOI: 10.7606/j.issn.1009-1041.2025.02.14
Year 2025 volume 45 Issue 2
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Article Info
doi: 10.7606/j.issn.1009-1041.2025.02.14
  • Receive Date:2024-01-15
  • Online Date:2026-09-11
  • Published:2025-02-15
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  • Received:2024-01-15
  • Revised:2024-02-29
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Affiliations
    1.College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
    2.College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China
    3.College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
    4.Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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