产量(工程)
人工神经网络
支持向量机
农业
农业工程
作物产量
计算机科学
机器学习
质量(理念)
作物
人工智能
均方预测误差
数学
农学
工程类
材料科学
生态学
哲学
认识论
冶金
生物
作者
Yuan Wang,Wenhao Zhao,Xiaoshuang Tang,Yang Liu,Hanyu Tang,Junwei Guo,Zhongqi Lin,Feng Huang
摘要
Rice is one of the most important grains in the world and its yield increase and quality improvement have always been the focus of research. Low temperature plasma (LTP) technology is a green agricultural technology, which can increase crop yield and improve crop quality. Accurate yield prediction and evaluation can promote the adjustment of agricultural production structure, the integration of agricultural resources and the healthy development of agricultural industry. It can also help to adjust crop management and commercial decisions (for example, to determine sales prices and marketing plans). In this paper, a plasma rice yield prediction model based on Bi-directional Long Short-Term Memory (Bi-LSTM) artificial neural network is constructed, which can accurately predict plasma rice yield. Compared with Multiple Linear Regression (MLR) and Support Vector Machine (SVM) methods, the results showed that the Bi-LSTM prediction model could well predict plasma rice yield, and the average error of predicted yield was 25 kg per mu (1mu = 666.67m2).
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