Vashisth Ananta, Goyal Avinash
Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Int J Biometeorol. 2023 Mar;67(3):539-551. doi: 10.1007/s00484-023-02434-2. Epub 2023 Jan 31.
Mustard is the second most important edible oilseed after groundnut for India. Adverse weather drastically reduces the mustard yield. Weather variables affect the crop differently during different stages of development. Weather influence on crop yield depends not only on the magnitude of weather variables but also on weather distribution pattern over the crop growing period. Hence, developing models using weather variables for accurate and timely crop yield prediction is foremost important for crop management and planning decisions regarding storage, import, export, etc. Machine learning plays a significant role as it has a decision support tool for crop yield prediction. The models for mustard yield prediction was developed using long-term weather data during the crop growing period along with mustard yield data. Techniques used for developing the model were variable selection using stepwise multiple linear regression (SMLR) and artificial neural network (SMLR-ANN), variable selection using SMLR and support vector machine (SMLR-SVM), variable selection using SMLR and random forest (SMLR-RF), variable extraction using principal component analysis (PCA) and ANN (PCA-ANN), variable extraction using PCA and SVM (PCA-SVM), and variable extraction using PCA and RF (PCA-RF). Optimal combinations of the developed models were done for improving the accuracy of mustard yield prediction. Results showed that, on the basis of model accuracy parameters nRMSE, RMSE, and RPD, the PCA-SVM model performed best among all the six models developed for mustard yield prediction of study areas. Performance of mustard yield prediction done by optimum combinations of the models was better than the individual model.
对于印度而言，芥菜是仅次于花生的第二重要的食用油籽。恶劣天气会大幅降低芥菜产量。天气变量在作物发育的不同阶段对作物的影响各不相同。天气对作物产量的影响不仅取决于天气变量的大小，还取决于作物生长期间的天气分布模式。因此，利用天气变量开发模型以准确及时地预测作物产量，对于作物管理以及有关储存、进出口等的规划决策至关重要。机器学习发挥着重要作用，因为它是一种用于作物产量预测的决策支持工具。利用作物生长期间的长期天气数据以及芥菜产量数据，开发了芥菜产量预测模型。用于开发模型的技术包括使用逐步多元线性回归（SMLR）和人工神经网络进行变量选择（SMLR - ANN）、使用SMLR和支持向量机进行变量选择（SMLR - SVM）、使用SMLR和随机森林进行变量选择（SMLR - RF）、使用主成分分析（PCA）和人工神经网络进行变量提取（PCA - ANN）、使用PCA和支持向量机进行变量提取（PCA - SVM）以及使用PCA和随机森林进行变量提取（PCA - RF）。对所开发模型进行了最优组合，以提高芥菜产量预测的准确性。结果表明，基于模型准确性参数nRMSE、RMSE和RPD，在为研究区域的芥菜产量预测所开发的六个模型中，PCA - SVM模型表现最佳。模型最优组合进行的芥菜产量预测性能优于单个模型。
