Active control of photosynthetic activities is important in plant physiological study. Although models of plant photosynthesis have been built at different scales, they have not been fully examined for their application in plant growth control. However, we do not have an infrastructure to support such experiments since current plant growth chambers usually use fixed control protocols. In our current paper, an open IoT-based framework is proposed. This framework allows a plant scientist or agricultural engineer, through an application programming interface (API), in a desirable programming language, (1) to gather environmental data and plant physiological responses; (2) to program and execute control algorithms based on their models, and then (3) to implement real-time commands to control environmental factors. A plant growth chamber was developed to demonstrate the concept of the proposed open framework.
The development of Adalia bipunctata larvae feeding on the grain aphid Sitobion avenae was investigated at 15, 20 and 25°C and two different levels of food supply. Increased temperatures accelerated development and reduced mortality rates. A reduced food supply slowed down development and increased mortality at all life stages. The total food intake of larvae ranged from 24-65 mg, which is equivalent to up to 190 aphids. Larvae compensated for low food supply by reducing development rates, high prey exploitation efficiencies, reaching up to 100%, and by high prey-biomass conversion efficiencies, reaching over 40%. The findings are discussed under the aspect of suitability of A. bipunctata as a biological control agent for greenhouse-specific aphid pest species.