LAUSR

It is important to obtain soil moisture content (SMC) in farmland, and soil surface images can be used to rapidly estimate SMC. The objective of this study was to propose a shadow removal algorithm to eliminate the effect of shadows in soil surface images, so as to improve the accuracy of SMC estimation. The structure of the proposed soil shadow generative adversarial networks (SS GAN) was a circulating network, which is an unsupervised method and does not require paired shadow image sets for network training. Four loss functions were defined for the network to effectively remove shadows and ensure texture detail and color consistency. This method is compared with traditional methods, supervised and unsupervised deep learning techniques by comparative experiments. Evaluations were made from visual and quantitative comparisons. Visually, the best shadow removal method was proved, it almost has no shadow boundaries or shadow areas visible for samples. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used to quantitatively compare shadow removal images with real non-shadow images. The PSNR and SSIM of SS GAN were 28.46 and 0.95 respectively, which are superior to other methods, indicating that the images processed by SS GAN were closer to the real non-shadow images. Field experiments results shown that SS GAN has excellent shadow removal performance in the self-developed vehicle-mounted detection system. In order to verify the improvement effect of shadow removal image on SMC estimation accuracy, further field test was conducted to estimate SMC. Compared with SMC estimation results before and after shadow removal, R 2 increased from 0.69 to 0.76, and root mean square error decreased from 1.39 to 0.94%. The results show that the proposed method can effectively remove the shadow of soil image and improve the accuracy of SMC estimation in farmland.