| The construction of drip\|irrigated winter wheat yield prediction model based on the fusion of canopy structure and time\|series spectral features |
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| DOI：10.7606/j.issn.1000-7601.2026.01.25 |
| Key Words: drip\|irrigation winter wheat yield prediction model hyperspectral leaf area index sensitive bands machine learning algorithms |
| Author Name | Affiliation | | GUO Xiaoshuai | College of Resources and Environment, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | LAI Ning | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | GENG Qinglong | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | LV Caixia | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | LI Yongfu | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | XIN Huinan | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | LI Na | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China | | CHEN Shuhuang | Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China
Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang 830091, China |
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| Abstract: |
| This study aimed to screen sensitive spectral indices and algorithms suitable for predicting the yield of drip\|irrigated winter wheat. Through two years of field trials using ‘Xindong 22’ as the experimental material, canopy spectral data were collected during four critical growth stages—jointing, booting, flowering, and grain filling—of drip\|irrigated winter wheat. Leaf area index (LAI) was measured. Pearson correlation analysis was conducted between 15 spectral indices and yield to identify sensitive indices characterizing yield. Yield prediction models were constructed using random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) algorithms. The results showed when modeling with spectral indices alone, the RF model during the grain filling stage achieved the highest accuracy (R2reached 0.70, RMSE was 927.86 kg·hm-2). After integrating spectral indices with LAI, the RF model during the grain filling stage performed optimally (R2 reached 0.73, RMSE was 910.06 kg·hm-2). Combining composite spectral indices with LAI and sensitive bands yielded the highest accuracy for the RF model during the grain filling stage (R2 reached 0.76, RMSE was 728.47 kg·hm-2). These findings indicate that the model constructed by adopting RF algorithm and integrating composite spectral indices, LAI, and sensitive bands can more accurately by predict the yield of drip\|irrigated winter wheat. |
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