Solving next-generation "food" and agricultural issues through JSOL's mathematical programming and advanced ICT
JSOL has been sophisticating harvest and shipping forecasts by designing solutions based on a growth model that gradually expands on “investments being smaller than profit”, and thereby, working towards improving the efficiency of overall management in agriculture, including aspects such as resource management and sales.
With the support and assistance of producers, distributers, food manufacturers, financiers, research institutes, etc., gradually, we can see data and technology that did not exist until now accumulating through the Scientist Network (Alliances).
JSOL's smart agriculture aims to bridge "food" and agriculture for our next generation around the world.
Issues that JSOL can help you solve
We would like to introduce and demonstrate cutting-edge technology to advance the sophistication of agriculture in line with actual conditions.
We would like to utilize the cultivation history and shipping data in farming operations.
We would like to use data to execute sales plans that are created based on experience.
Mathematical programming and collaborative research achievements (implemented with farming corporations and research institutes)
We offer shipping forecast models and data analysis for the entire production area based on the knowledge and data of agricultural sites.
Sophisticated and efficient agricultural management
While focusing on mathematical programming (harvest forecast, demand forecast, risk forecast), we aim to collaborate with agriculturalists, industries such as distribution, wholesale and retail, and financial institutions and insurance companies to build a system that improves the sophistication, efficiency and profitability of overall agricultural management, including production management, sales management, ordering management and financial management.
JSOL and joint research institutes implement mathematical programming (to return demand, harvesting and shipment risk forecasts) by analyzing cultivation history, shipment schedule/shipment data, breed, area, farmer characteristics, important risks found after discussions, etc.
Actual example of mathematical programming
JA Kagawa Prefecture: Efforts regarding shipping forecast
The shipment amount is predicted by statistical analysis based on past data (cultivation history data, shipping data, yield, etc.) in open field cultivation,
We have designed a shipment forecasting model that reduces the time required for growth surveys and allows even inexperienced personnel to grasp the shipping prospect in advance.
By using this model, strategic pricing can be set and it will contribute to increasing the income of farmers. In the sales side, we are aiming to raise the income of farmers by promoting strategically advantageous sales. (For details, please refer to the news release "Consultation services launched for designing the shipment prediction model for agricultural products" and "Development and verification started for shipment prediction model for broccoli" in the "Related Links" below. (these are in Japanese))
Shiga University, RIKEN, Higashibaba Farm: Pioneering Business
As one of the “Sakigake” projects of the Ministry of Education, we worked toward the development of growth prediction technology based on mathematical analysis methods that use gene expression data.
In this effort, we will build a growth prediction technology based on environmental data measured over time during crop cultivation and the gene expression data obtained from the crop growth process provided by RIKEN.
Using this technology to predict growth conditions and yields will help optimize production and sales, thereby reducing waste. It will also be useful in sophisticating other business management areas like staffing. (For details, please refer to the news release "JSOL and Shiga University jointly work toward development of growth prediction technology using gene expression data" in the "Related Links" below. (In Japanese))
For inquiries related to this solution
Digital Innovation Business Unit