Satellite imagery, big data, IoT and deep learning techniques for wheat yield prediction in Morocco

Abdelouafi Boukhris, Antari Jilali, Abderrahmane Sadiq
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Abstract

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0.00036 and 0.99 respectively.
The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc., using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.
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卫星图像、大数据、物联网和深度学习技术用于摩洛哥小麦产量预测
在有效管理资源和确保营养一致性方面,准确预测作物产量至关重要。人工智能技术与卫星图像同步发展,已成为现代预测作物产量的有效方法。我们使用了两类数据:空间数据和时间数据。空间数据来自卫星图像,并使用 ArcGIS 进行处理,以提取基于 NDVI 和 NWDI 等指数的作物数据。时间数据来自农业传感器,如温度传感器、雨量传感器、降水传感器和土壤水分传感器。在我们的案例中,我们使用哨兵 2 号卫星来提取植被指数。我们使用物联网系统,特别是 Raspberry Pi B+ 来收集和处理来自传感器的数据。收集到的所有数据都会存储到 NoSQL 服务器中进行分析和处理。在处理作物推荐系统时,我们使用了多种机器学习和深度学习算法,如逻辑回归、KNN、决策树、支持向量机、LSTM 和 Bi-LSTM 等。我们使用的 GRU 深度学习模型性能最佳,该模型的 RMSE 和 R2 分别为 0.00036 和 0.99。我们论文的主要贡献是利用物联网、卫星图像获取空间数据,并使用传感器获取时间数据,开发了一种可预测小麦、玉米等多种作物产量的新系统。与其他仅使用遥感数据或时间数据的论文不同,我们是第一篇结合空间数据和时间数据,基于深度学习算法预测作物产量的论文。我们创建了一个电子监测作物产量预测系统,帮助农民跟踪作物的所有信息,并在移动应用程序中显示预测结果。该系统可帮助农民做出更有效的决策,提高作物产量。摩洛哥的小麦主产区位于沙维雅、阿布达、豪斯、塔德拉、加尔布和赛斯平原和高原的雨水灌溉区。我们研究了拉巴特-萨莱、非斯-梅克内斯、卡萨布兰卡-塞塔特这三个著名的小麦主产区。
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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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