To manage rice cropping effectively, managers need to predict crop growth. We developed a cloud-based model to predict heading of the main cultivars grown in Chiba Prefecture from AMeDAS data and to calculate crop growth stages and appropriate work periods. We developed the “Deruta” interface to enable viewing of this information on smartphones and tablets. Heading dates in 15 fields in Chiba had a root mean square error, an indicator of the accuracy of numerical prediction, of 2.99 days. Of those predicted by the model, 86.7% lay within ±3 days, the target value. Our results show that it is easy to generate and transmit crop information via information and communications technology. “Deruta” will serve as a model for the forthcoming development of models for predicting crop growth and pest outbreaks.

To manage rice cropping effectively, managers need to predict crop growth. We created the “Deruta” smartphone interface and a cloud-based system for predicting heading time and work periods for the main cultivars of paddy rice grown in Chiba Prefecture. “Deruta” presents dates predicted for a selected cultivar and transplanting date from AMeDAS data. It was developed with the intention of simplifying development, operation, and use. We asked 15 agricultural managers in Chiba Prefecture to test it. They found it is easy to operate, and as the accuracy of predicting heading date is within ±3 days, assessed it as effective for planning and timely implementation of management tasks, concluding that they would like to continue using it. The innovation adoption process of Rogers showed that the testers found “Deruta” to have attributes that accelerate adoption and to support sustainability. When we tested its use without limiting the number of users, many accesses were made, demonstrating the high degree of acceptability.

To identify what would prompt rice farming corporations to introduce smart farming technologies, we compared their strengths and weaknesses with those of their competitors (i.e., we evaluated their managerial awareness). Using the results of a nationwide survey of agricultural corporations, we analyzed the intention to introduce smart farming technology by decision tree analysis. Under the category of “Measuring crop growth by drones and satellites”, corporations evaluated themselves as excellent in “production and processing technology”, “suppliers, trust of local communities and brand”, and “sales and marketing”, and showed a strong intention to introduce smart farming technologies. Under the category of “Automation and robots in crop cultivation machinery work”, corporations evaluated themselves as less than excellent in “human resource development” and “production/processing technology”, and again showed intention to introduce smart farming technologies. These results suggest that to advance the adoption of smart farming technologies, it is effective to conduct extension activities in consideration of a corporation’s brand, marketing activities, human resource development, and production/processing technologies.

With the aging population and lack of succession in Japanese agriculture, the use of agricultural drone is expanding in work such as chemical spraying. To support planning of crop protection work in large agricultural corporations, we developed a work planning system to simulate the work time of agricultural drones, named the “Agri Drone Workplan Supporter” (ADWS). ADWS is a Python QGIS plugin that uses field polygon data and local road network data to calculate the shortest travel route between fields and consequent work time within a set upper daily limit. Reflecting real conditions, the calculated work time accounts for not only the work time in the fields and the travel time between fields, but also the time of loading and unloading of equipment, battery replacement, and chemical replenishment. Considering the risk of precipitation, the ADWS could also calculates the daily workable index based on the historical precipitation data. Work plans created by ADWS could save time for users at the planning stage, thereby improve the efficiency of agricultural production.