Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
National Engineering Research Center for Agroecological Big Data Analysis & Application, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China.
Comput Intell Neurosci. 2022 Aug 9;2022:5614974. doi: 10.1155/2022/5614974. eCollection 2022.
With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties to increase crop yields has become a consensus among agricultural researchers. However, there are still many problems in existing works, such as limited crop phenotypic data and the poor performance of artificial intelligence models. In this regard, we take maize as an example to collect a large amount of environmental climate and crop phenotypic traits data at multiple experimental sites and construct an extensive dataset. Then, we introduce a graph neural network model to learn crop suitability evaluation and finally achieve a good evaluation effect. The evaluation results of the model can not only provide a reference for expert evaluation but also judge the suitability of the variety to other test trial sites according to the data of the current one, so as to guide future breeding experiments.
随着全球人口的不断增长，粮食产量不足已成为大多数国家亟待解决的问题。目前，利用人工智能技术提高土地和作物品种之间的适配性以提高作物产量已成为农业研究人员的共识。然而，现有工作中仍存在许多问题，例如作物表型数据有限和人工智能模型性能不佳。针对这一问题，我们以玉米为例，在多个实验点收集了大量的环境气候和作物表型特征数据，并构建了一个广泛的数据集。然后，我们引入图神经网络模型来学习作物适应性评估，最终取得了良好的评估效果。该模型的评估结果不仅可以为专家评估提供参考，还可以根据当前数据判断品种对其他试验点的适应性，从而指导未来的育种实验。
