Precise prediction of agricultural production output is crucial for farmers, policymakers, and the Farming-related industry. This article introduces a novel methodology to crop yield forecasting using a capsular neural network equipped with Conv-LSTM and attention mechanism. Our model combines the strengths of 3DCNN, and Conv-LSTM, which can capture the temporal dependencies and 3D features of crop yield data, and attention mechanism, which Can prioritize the most significant characteristics for making predictions. We evaluated CACN on a sizable collection of data of soybean crop yield in the United States from 2003 to 2019 and evaluated against various cutting-edge deep learning models. The outcomes indicate that our suggested approach surpasses other models in performance in terms of RMSE, correlation coefficient, and prediction error map. Specifically, our model achieved approximately 14 % improvement in terms of RMSE, compared to the state-of-the-art model Deep-Yield. Our model also demonstrated the ability to extract more meaningful features and capture the complex relationships between crop yield data and meteorological variables. Overall, our proposed method shows great potential for accurate and efficient crop yield forecasting and can be applied to other crops and regions.

中文翻译：

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囊状注意力 Conv-LSTM 网络 （CACN）：一种基于多光谱影像的作物产量估算深度学习结构


精确预测农业生产产出对于农民、政策制定者和农业相关行业至关重要。本文介绍了一种使用配备 Conv-LSTM 和注意力机制的胶囊神经网络预测作物产量的新方法。我们的模型结合了 3DCNN 和 Conv-LSTM 的优势，它可以捕捉作物产量数据的时间依赖性和 3D 特征，以及注意力机制，它可以优先考虑最重要的特征进行预测。我们根据 2003 年至 2019 年美国大豆作物产量的大量数据评估了 CACN，并根据各种尖端的深度学习模型进行了评估。结果表明，我们建议的方法在 RMSE、相关系数和预测误差图方面的性能优于其他模型。具体来说，与最先进的模型 Deep-Yield 相比，我们的模型在 RMSE 方面实现了大约 14% 的改进。我们的模型还展示了提取更有意义的特征并捕获作物产量数据和气象变量之间复杂关系的能力。总体而言，我们提出的方法显示出准确和高效的作物产量预测的巨大潜力，可以应用于其他作物和地区。