Dynamic Bayesian network for crop growth prediction in greenhouses Abstract   The paper presents an InternetofThings based agricultural decision support system for crop growth. A dynamic Bayesian network (DBN) relates indicative parameters of crop development to environmental control parameters via unobserved (hidden) Markov states. The expectationmaximization algorithm is used to track the states and to learn the parameters of the DBN. The steady state information is then used to derive a predictor for the measurement data a few days ahead. The proposed DBN avoids timeconsuming training cultivation cycles, as only data of the current cultivation cycle are available to the algorithm.  Three cultivation cycles of lettuce have been used to test the performance of the proposed DBN. The environmental parameters were temperature, solar irradiance and vaporpressure deficit. The measurement data include evapotranspiration at granularity equal one day, and leafarea index and dry weight, at granularity equal one week. It turned out that accurate measurement data prediction a few days ahead is possible even if the number of data samples is low.

Interactive Cultivation System for the Future IoTBased Agriculture As initiatives to increase Japanu0027s declining food selfsufficiency rate and revitalize the field of agriculture, the concept of smart agriculture and urban agriculture are currently being implemented. Automation via Artificial Intelligence is expected to overcome the labor shortage in the agricultural industry. However, the number of skilled farmers who can contribute to gathering crop growth data required for machine learning is restricted, and these data are also limited to localand environmentalbased conditions. In this paper, we propose a system for obtaining composite growth data in various environments and crops targeted for home gardens and paddy fields. An interactive cultivation sensing system consisting of IoTbased technologies is designed and realized to ensure the continuous growth of crops in optimum conditions daily. With this, progress will be made in determining the efficient cultivation conditions for machine learning, and in finding solutions to future problems of agriculture.