Artificial Intelligence and Satellite Based Remote Sensing can be used to Predict Soybean (Glycine max) Yield
Artificial Intelligence and Satellite Based Remote Sensing can be used to Predict Soybean (Glycine max) Yield
复制标题
人工智能和卫星遥感可用于预测大豆 (Glycine max) 产量
DOI：
10.1002/agj2.21473
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发表时间：
2023
期刊：
影响因子：
2.1
通讯作者：
Clay, David E.
中科院分区：
文献类型：
--
作者：
Joshi, Deepak R.;Clay, Sharon A.;Sharma, Prakriti;Rekabdarkolaee, Hossein Moradi;Kharel, Tulsi;Rizzo, Donna M.;Thapa, Resham;Clay, David E.
由于大豆（甘氨酸max）植株、豆荚和种子/豆荚的人工计数不适合大豆产量预测，需要替代方法。因此，目的是确定基于卫星遥感的人工智能（AI）模型是否可用于预测大豆产量。在本研究中，建立了多个基于遥感的大豆生育期人工智能模型，从VE/VC（植株出苗期）到R6/R7（全种至初熟期）。基于PlanetScope卫星在六个生长阶段收集的蓝、绿、红和近红外反射率数据，研究了深度神经网络（DNN）、支持向量机（SVM）、随机森林（RF）、最小绝对收缩和选择算子（LASSO）和AdaBoost预测大豆产量的能力。将2019年和2021年3个不同领域的遥感和大豆产量监测数据汇总为24282个网格单元，网格单元的尺寸为10 m × 10 m。模型之间的比较表明，深度神经网络优于其他模型。随着作物从VE/VC成熟到R4/R5，模型的ther2值从0.26增加到0.70以上。这些发现表明，在不同生育期收集的遥感数据可以组合用于大豆产量预测。此外，还需要进行额外的工作，以评估该模型利用未用于训练模型的农田的植被指数（VIs）数据预测大豆产量的能力。
Because the manual counting of soybean (Glycine max) plants, pods, and seeds/pods is unsuitable for soybean yield predictions, alternative methods are desired. Therefore, the objective was to determine if satellite remote sensing‐based artificial intelligence (AI) models could be used to predict soybean yield. In the study, multiple remote sensing‐based AI models were developed for soybean growth stage ranging from VE/VC (plant emergence) to R6/R7 (full seed to beginning maturity). The ability of the deep neural network (DNN), support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), and AdaBoost to predict soybean yield, based on blue, green, red, and near‐infrared reflectance data collected by the PlanetScope satellite at six growth stages, was determined. Remote sensing and soybean yield monitor data from three different fields in 2 years (2019 and 2021) were aggregated into 24,282 grid cells that had the dimensions of 10 m by 10 m. A comparison across models showed that the DNN outperformed the other models. Moreover, as crops matured from VE/VC to R4/R5, theR2value of the models increased from 0.26 to over 0.70. These findings indicate that remote sensing data collected at different growth stages can be combined for soybean yield predictions. Moreover, additional work needs to be conducted to assess the model's ability to predict soybean yield with vegetation indices (VIs) data for fields not used to train the model.