On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco

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Khadija Meghraoui
Imane Sebari
Saloua Bensiali
Kenza Ait El Kadi


Agriculture is a key sector in the global economy, and crop yield is a primary element in this field. Its estimation represents a troublesome task considering its link to market planning. Different techniques have been tested to estimate crop yield. However, they present several limits in terms of human effort and time. The agricultural world is now experiencing a digital revolution, for example the utilization of UAV (Unmanned Aerial Vehicle) provides high-resolution images of crops. Besides this, artificial intelligence techniques namely, DL (Deep Learning) enables today a revolution of the flow processing and improves the resulting information. This paper presents a case study of wheat yield estimation in Morocco using UAV multimodal remote sensing data and 3D CNN (3D Convolutional Neural Network). Our data was acquired during the growth season, and predicted yield was compared to in situ data. Based on our results, the RMSE value was 0.175 qx/ha. Our conclusions highlight the efficient role of convolutional neural network in capturing features in raw image data and the importance to improve resulting predictions by acquiring data over the entire period of growth, and the necessity to choose a temporal architecture, which is able to process temporal variations.

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Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329


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Meghraoui, K., Sebari, I., El Kadi, K. A., & Bensiali, S. (2022). Towards 3D CNN for precise crop yield estimation using multimodal remote sensing data: Case study of wheat in Morocco. Advanced Engineering Days (AED), 3, 79-81.