SDGs（Sustainable Development Goals:持続可能な開発目標）において，健康・飢餓・気候変動問題の解決，産業の発展に貢献できる確認技術の開発が求められています。特に農業分野においては，世界的な人口増加に対する食糧の安定供給を目指し，農業のIoT（Internet of Things）化が

There is a lot of previous research on cultivation management based on mathematical engineering. In some previous research, the expected profit of cultivation management using sensors is maximized under the condition that growing states are unknown. Considerations of cost constraints are needed in real agriculture. But in the previous research, cost constraints are not considered. So, in this research, cost constrained cultivation management using sensors is studied. A new cultivation management method which maximizes the expected profit of cost constrained cultivation management using sensors is proposed. In the proposed method, multiple agricultural fields are managed. The cultivation management problem is modeled by Markov decision processes as previous research. The expected profit is maximized with reference to a Bayes criterion using dynamic programming method. The effectiveness of the proposed method is shown by some computational examples. Adaptive cultivation action selection, cost adjustment on the time axis and cost adjustment between the agricultural fields are confirmed in the computational examples.

The growth and development of embryophytes is deeply influenced by environmental stimuli, such as light, temperature and soil nutrients. Understanding the mechanisms underlying the growth response of plants to environmental stimuli is crucial for agriculture. In this study, we examined the morphology of a flowering plant, Arabidopsis thaliana, using microfocus X-ray computed tomography (µCT), which enables non-destructive analysis of the external and internal structures of plants. Three-dimensional (3D) images of the plant, which were reconstructed from X-ray scanned data, clearly showed the shapes of its leaves, stems, and buds from any angle. At a higher magnification, the mCT also revealed the small hair-like structures called trichomes on the Arabidopsis leaf epidermis. However, motion artifacts found in the 3D-reconstructed images indicated that plant's growth rate was faster than scanning speed. Thus, scan parameters must be accordingly optimized. Additionally, CT-based 3D printing can be used to design micro devices that can be further used to monitor plant growth. These results suggest that µCT is a useful technique for analyzing morphology of growing plants.

The compact chlorophyll measurement system was developed to quantify chlorophyll a and chlorophyll b for agriculture applications. The developed system can identify spectral changes in light of wavelength 600 nm with a sensitivity of 0.01 nm or more. The proposed system was evaluated to confirm the chlorophyll content and chlorophyll a/b ratio using the chlorophyll a and chlorophyll b standard reagents of spinach. The system detected chlorophyll content with a sensitivity of 9.37 nA/µM. The sensor current ratio changed linearly to the transmitted light spectrum, which changed with the difference in the absorption wavelengths of chlorophyll a and chlorophyll b. As chlorophyll a/b increased by 1, a linear result was obtained in which the sensor current ratio decreased by -0.0129. It was demonstrated that chlorophyll can be quantitatively measured using the proposed compact chlorophyll measurement system. In the future, the proposed system is expected to be integrated with IoT technology to improve the productivity and quality of crops at agricultural sites.