The researchers at Okayama University in Japan have successfully estimated rice yields using a convolutional neural network model and pre-harvest paddy field images. They established a database containing over 20,000 images of paddy fields and yield data, and developed a CNN model to estimate the yield for each image. This model explains approximately 68-69% of the yield variability and can accurately predict yields during the maturation period, demonstrating the potential for monitoring rice yields. This research aids in improving paddy field management and accelerating breeding programs, contributing to global food production and sustainable development.