Paper
8 November 2024 Prediction method of wheat yield in typical crop area of Hefei based on neural network
Yun Jiang, Feng Chen, Yue Pan, Gen Wang, Bo Song
Author Affiliations +
Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161P (2024) https://doi.org/10.1117/12.3049641
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
China is a country with a large population. Ensuring food security is related to China 's national economy and people's livelihood and social stability. Wheat is the most widely planted, the largest area and the most productive food crop in the world. The timely estimation of wheat yield has a significant impact on crop production, food prices and food security. Wheat yield is one of the important indicators to evaluate agricultural productivity. In view of the difficulty of manual estimation of wheat yield, it is proposed to apply convolutional neural network to wheat yield estimation, so as to provide reference for agricultural productivity estimation and guide agricultural production management decision-making. In this paper, Anhui Shuyu Ecological Farm and Changfeng Lixin Family Farm were selected as the research objects, and the wheat distribution map of the farm was obtained by using the convolutional neural network. It is estimated that the annual output of the farm in 2021 will be 317065kg and 790210kg, respectively, and the statistical data of 333750kg and 858920kg provided by Anhui Shuyu Ecological Agriculture Co., Ltd. and Changfeng Lixin Family Farm. The error is 4.9 % and 7.9 %, respectively. which verifies the effectiveness of the estimation method.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yun Jiang, Feng Chen, Yue Pan, Gen Wang, and Bo Song "Prediction method of wheat yield in typical crop area of Hefei based on neural network", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161P (8 November 2024); https://doi.org/10.1117/12.3049641
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KEYWORDS
Neural networks

Convolutional neural networks

Data acquisition

Remote sensing

Satellites

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