Utilizing Convolutional Neural Networks (CNNs) for Persimmon Irrigation Decision-Making: A Case Study in the Gojo Yoshino Region, Nara Prefecture, Japan

Abstract

This study aimed to develop and evaluate a model for persimmon irrigation decision-making using convolutional neural networks (CNNs) based on leaf image data. The leaf moisture data collected in the field and corresponding soil moisture measurements were gathered from the Gojo Yoshino region, recognized as the primary persimmon-producing area in Nara Prefecture, Japan's second-largest persimmon-producing prefecture. The findings demonstrate that the constructed CNN model can successfully identify water stress levels in persimmon trees from leaf image data. However, there are limitations to the model's performance and scope for improving accuracy. The model's capability enables remote irrigation decision-making by integrating field-acquired leaf images into edge devices for on-site processing. When integrated with ongoing developments in remote irrigation systems, this technology has the potential to automate irrigation practices, thereby offering substantial labor-saving benefits.