Graduate Program in Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.
Excellence Centre in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai, 50200, Thailand.
Sci Rep. 2022 Aug 25;12(1):14488. doi: 10.1038/s41598-022-18635-5.
Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.
作物产量及其预测在农业生产规划中至关重要。本研究使用人工神经网络（ANN）和多元线性回归（MLR）来调查和预测阿拉比卡咖啡的产量，以匹配市场需求。收集了 2004 年至 2018 年期间最近 180 个月的六个变量的数据，包括面积、生产力区、降雨量、相对湿度以及最低和最高温度。樱桃咖啡作物的预测产量逐年持续增加。从数据集来看，ANN 的 R 和 RMSE 的预测精度分别为 0.9524 和 0.0784 吨。ANN 模型在确定樱桃咖啡产量方面显示出了潜力。
