Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images

By Qi Yang, Liangsheng Shi, Jinye Han, Y. Zha, Peng Zhu. Field crops research (Print), 2019

341 citations recorded by the source corpus.

Abstract Forecasting rice grain yield prior to harvest is essential for crop management, food security evaluation, food trade, and policy-making. Many successful applications have been made in crop yield estimation using remotely sensed products, such as vegetation index (VI) from multispectral imagery. However, VI-based approaches are only suitable for estimating rice grain yield at the middle stage of growth but have limited capability at the ripening stage. In this study, an efficient convolutional neural network (CNN) architecture was proposed to learn the important features related to rice grain yield from low-altitude remotely sensed imagery. In one major region for rice cultivation of Southern China, a 160-hectare site with over 800 management units was chosen to investigate the ability of CNN in rice grain yield estimation. The datasets of RGB and multispectral images were obtained by a fixed-wing, unmanned aerial vehicle (UAV), which was mounted with a digital camera and multispectral sensors. The network was trained with different datasets and compared against the traditional vegetation index-based method. In addition, the temporal and spatial generality of the trained network was investigated. The results showed that the CNNs trained by RGB and multispectral datasets perform much better than VIs-based regression model for rice grain yield estimation at the ripening stage. The RGB imagery of very high spatial resolution contains important spatial features with respect to grain yield distribution, which can be learned by deep CNN. The results highlight the promising potential of deep convolutional neural networks for rice grain yield estimation with excellent spatial and temporal generality, and a wider time window of yield forecasting.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/d531209f65e0d492f19244389d0e32c7426dc4b9

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In Analytical Chemistry

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In Ecology

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Citations in the corpus

• Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications (2020, background): “The CNN network learns the significant spatial features concerning the crop yield from the high spatial resolution RGB image [42].”
• County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model (2019, methodology): “found that the CNN trained by RGB and multispectral datasets perform much better than a VI-based regression model for rice grain yield estimation at the ripening stage [24].”
• High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, methodology): “[238] used deep CNNs to estimate rice yield at the ripening stage from UAV, RGB, and multispectral imaging data and demonstrated that CNNs perform better than VI-based models for rice yield estimation ( .”