Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis

Abstract

Rice grain production is important for the world economy. Determining the moisture content of the grains, at several stages of production, is crucial for controlling the quality, safety, and storage of the grain. This work inspects how well rice images from global and local descriptors work for determining the moisture content of the grains using artificial vision and intelligence techniques. Three sets of images of rice grains from the INIAP 12 variety (National Institute of Agricultural Research of Ecuador) were captured with a mobile camera. The first one with natural light and the other ones with a truncated pyramid-shaped structure. Then, a set of global descriptors (color, texture) and a set of local descriptors (AZAKE, BRISK, ORB, and SIFT) in conjunction with the dominate technique bag of visual words (BoVW) were used to analyze the content of the image with classification and regression algorithms. The results show that detecting humidity through images with classification and regression algorithms is possible. Finally, f1-score values of at least 0.9 were accomplished for global color descriptors and of 0.8 for texture descriptors, in contrast to the local descriptors (AKAZE, BRISK, and SIFT) that reached up to an f1-score of 0.96.

Application of Image Processing in Agriculture

“…Gonçalves et al [8] used mobile devices to acquire RGB images for pest monitoring in viticulture, with further analysis using five different deep-learning models. Cabrera et al [9] also used images collected with mobile cameras to acquire images of rice grains, aiming to determine the moisture content with classification and regression algorithms. Renfroe-Becton et al [10] used images collected with smartphones and RGB cameras to develop a technique for diagnosing peanut foliar symptoms based on image classification and regression models.…”