Development of a 2D Image-Based Rice Yield Prediction Framework Using an Image-Based Reconstruction Technique

Asian countries, which account for more than 60% of global rice consumption, are expanding the adoption of precision agriculture technology using image sensors to increase the profitability of rice production. This requires the development of technology to process 2D images that can be obtained by individual farmers instead of expensive 3D scanners. This study aims to quantitatively extract grain-level shape information necessary for yield prediction using 2D rice panicle images. To achieve this, a framework for predicting rice panicle yield from 2D images that uses a convolutional neural network (CNN) to detect grains is developed. Unlike existing approaches that measure grain length, width, and thickness using vernier calipers or 3D scanners to reconstruct 3D volume and estimate yield factors through volume-weight relationships, this methodology utilizes panicle length and projected grain area, which are relatively stable shape indices derived from 2D panicle images, to accurately describe weight variation within the same variety (e.g., Huaidao, Sidao, Suxiu, Jingjing). Experiments are conducted using panicle image data of Chinese Japonica rice varieties collected in Jiangsu Province, China. The proposed methodology demonstrates high prediction accuracy, with coefficients of determination ranging from 0.89 to 0.96, by combining panicle length and projected grain area information.