Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China

Author:

Lu Jian1,Li Jian1,Fu Hongkun2,Tang Xuhui3,Liu Zhao4ORCID,Chen Hui4,Sun Yue4,Ning Xiangyu4

Affiliation:

1. Institute of Smart Agriculture, Jilin Agricultural University, Changchun 130118, China

2. College of Agriculture, Jilin Agricultural University, Changchun 130118, China

3. College of Information Technology, Jilin Agricultural University, Changchun 130118, China

4. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Abstract

The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional models such as RF, XGBoost, and CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, and photosynthetically active parameters, our research examines the model’s capacity to capture essential spatial and temporal variations. The CNN-LSTM-Attention model integrates Convolutional Neural Networks, Long Short-Term Memory, and an attention mechanism to effectively process complex datasets and manage non-linear relationships within agricultural data. Notably, the study explores the potential of using kNDVI for predicting yields of multiple crops, highlighting its effectiveness. Our findings demonstrate that advanced deep-learning models significantly enhance yield prediction accuracy over traditional methods. We advocate for the incorporation of sophisticated deep-learning technologies in agricultural practices, which can substantially improve yield prediction accuracy and food production strategies.

Funder

Changchun Science and Technology Development Program

Jilin Province Science and Technology Development Program

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hybrid LSTM and SVM Method Rice Yield Prediction in Densely Populated Areas;2024 International Electronics Symposium (IES);2024-08-06

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