Batool Dania, Shahbaz Muhammad, Shahzad Asif Hafiz, Shaukat Kamran, Alam Talha Mahboob, Hameed Ibrahim A, Ramzan Zeeshan, Waheed Abdul, Aljuaid Hanan, Luo Suhuai
Department of Computer Engineering, University of Engineering and Technology, Lahore 58590, Pakistan.
Department of Computer Science, New Campus, University of Engineering and Technology, Lahore 58590, Pakistan.
Plants (Basel). 2022 Jul 25;11(15):1925. doi: 10.3390/plants11151925.
Tea ( L.) is one of the most highly consumed beverages globally after water. Several countries import large quantities of tea from other countries to meet domestic needs. Therefore, accurate and timely prediction of tea yield is critical. The previous studies used statistical, deep learning, and machine learning techniques for tea yield prediction, but crop simulation models have not yet been used. However, the calibration of a simulation model for tea yield prediction and the comparison of these approaches is needed regarding the different data types. This research study aims to provide a comparative study of the methods for tea yield prediction using the Food and Agriculture Organization (FAO) of the United Nations AquaCrop simulation model and machine learning techniques. We employed weather, soil, crop, and agro-management data from 2016 to 2019 acquired from tea fields of the National Tea and High-Value Crop Research Institute (NTHRI), Pakistan, to calibrate the AquaCrop simulation model and to train regression algorithms. We achieved a mean absolute error () of 0.45 t/ha, a mean squared error () of 0.23 t/ha, and a root mean square error () of 0.48 t/ha in the calibration of the AquaCrop model and, out of the ten regression models, we achieved the lowest of 0.093 t/ha, of 0.015 t/ha, and of 0.120 t/ha using 10-fold cross-validation and of 0.123 t/ha, of 0.024 t/ha, and of 0.154 t/ha using the XGBoost regressor with train test split. We concluded that the machine learning regression algorithm performed better in yield prediction using fewer data than the simulation model. This study provides a technique to improve tea yield prediction by combining different data sources using a crop simulation model and machine learning algorithms.
茶（茶树）是全球除水之外消费量最高的饮品之一。几个国家从其他国家大量进口茶叶以满足国内需求。因此，准确及时地预测茶叶产量至关重要。先前的研究使用统计、深度学习和机器学习技术来预测茶叶产量，但尚未使用作物模拟模型。然而，需要针对不同数据类型对用于茶叶产量预测的模拟模型进行校准，并比较这些方法。本研究旨在对使用联合国粮食及农业组织（FAO）的AquaCrop模拟模型和机器学习技术进行茶叶产量预测的方法进行比较研究。我们使用了2016年至2019年从巴基斯坦国家茶叶和高价值作物研究所（NTHRI）的茶园获取的天气、土壤、作物和农业管理数据，来校准AquaCrop模拟模型并训练回归算法。在AquaCrop模型的校准中，我们实现了平均绝对误差（MAE）为0.45吨/公顷，均方误差（MSE）为0.23吨/公顷，以及均方根误差（RMSE）为0.48吨/公顷。在十个回归模型中，使用10折交叉验证时，我们实现了最低的MAE为0.093吨/公顷，MSE为0.015吨/公顷，RMSE为0.120吨/公顷；使用训练测试分割的XGBoost回归器时，MAE为0.123吨/公顷，MSE为0.024吨/公顷，RMSE为0.154吨/公顷。我们得出结论，机器学习回归算法在使用比模拟模型更少的数据进行产量预测时表现更好。本研究提供了一种通过使用作物模拟模型和机器学习算法结合不同数据源来提高茶叶产量预测的技术。
