The Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists held an international symposium titled “Basic and Applied Sciences for Intelligent Plant Production Systems” on the 9th of September 2008, Ehime University, Matsuyama, Japan. From that symposium, this special issue for “Basic and Applied Sciences for Intelligent Plant Production Systems” has been organized, and 5 review papers will be published in the present special issue. The review papers cover the fundamentals of plant growth factories, especially, topics related to the concept of “Speaking Plant Approach” and greenhouse automation technology.
Plant control systems are usually characterized by complexity and uncertainty. A skilled grower can deal well with various plants and yield good products using intuition and visual information, communicating with the plants. To realize such tasks scientifically, it is important to measure physiological responses of the plant using sensors and then use that information for environmental control for optimization. Such an approach is called the ‘speaking plant approach (SPA)’. This approach is also easy to apply storage processes. Such an application could be a ‘speaking fruit approach (SFA)’. Measurement, identification and optimization of plant responses (or fruit responses), as affected by environmental factors, are important tasks for the SPA (or SFA). It is, however, difficult to sufficiently address these tasks because plant responses are quite complex and uncertain, but intelligent control techniques can facilitate such tasks. This paper presents the concepts of SPA and SFA, an intelligent control technique for realizing the SPA (or SFA), and their applications for optimizing cultivation and storage processes, aimed at qualitative improvement of plants and fruits. In the cultivation process, the net photosynthetic rate of a tomato was maximized by the drainage and supply operation of a hydroponic solution and tomato growth during the seedling stage was optimized by nutrient concentration of a hydroponic solution. For the storage process, the rate of water loss in tomatoes was minimized by temperature. The optimal value of each environmental factor was not assigned a constant value but rather an optimal combination of maximum and minimum values. These results suggest that the optimal application of environmental stress to the plant allows for qualitative improvement of plants and fruits.
Electrospray has been used as one of the most powerful techniques for the analysis of biological samples. Recently, we have developed the probe electrospray (PESI) (modified version of the conventional electrospray) that uses the fine solid needle. In this method, the probe needle moves up and down along the vertical axis by a motor-driven system. At the highest position of the probe needle, electrospray is generated by applying a high voltage. In this study, we used PESI directly to the biological samples such as human milk, bovine milk, beverages, mouse brains, flower petals, plant leaves, bananas, tulip bulbs, etc. Strong ion signals for almost all samples were obtained. The amount of liquid sample picked up by the needle is as small as a few pL or less, making the PESI one of the most promising less-invasive analysis and imaging for biological samples. PESI may be useful as versatile and ready-to-use semi-on line analyses in the fields of agriculture, food science, medicine, pharmaceutical, etc. An example of imaging for mouse brain is demonstrated.
This review focuses on the monitoring of crop health status at greenhouse scale, based on the measurement of volatile organic compounds (VOCs) emitted from the plants. The review includes the most important factors that affect the emission of these VOCs from greenhouse crops. Since both, stress factors as well as non-stress factors have an effect on the emission, they are covered separately. The review provides an overview of processes that affect the gas balance of plant VOCs in the greenhouse including the loss processes. These processes are considered as important since they contribute to the time-dynamic concentration profiles of plant-emitted VOCs. In addition, we review the most popular techniques currently in use to measure volatiles emitted from plants with emphasis on greenhouse application. Dynamic sampling in combination with gas chromatography coupled to mass spectrometry is considered as most appropriate method for application at greenhouse scale. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in greenhouse practice. However, to apply such an instrument at greenhouse-scale remains a challenge, mainly due to the high costs associated with it.
Chlorophyll fluorescence imaging is useful as a non-destructive method for evaluating photosynthetic function of plants. A dynamic change in chlorophyll fluorescence intensity, known as the chlorophyll fluorescence induction phenomenon, can be observed by illuminating a dark-adapted green leaf with a stable intensity excitation light. The time course of the chlorophyll fluorescence intensity during this phenomenon, presented as an induction curve, varies depending on the health of the plant. Imaging of the chlorophyll fluorescence induction phenomenon may be used to monitor the health status of plants. In this review, we introduce studies on our chlorophyll fluorescence imaging system for plant health monitoring of tomato crops grown in a greenhouse. We first developed a prototype of the imaging system to confirm performance on detection of artificially induced light stress in a single leaf and whole plant. Based on the successful detection of photosynthetic dysfunction caused by light stress using the prototype, we applied our chlorophyll fluorescence imaging system to measurements of the chlorophyll fluorescence induction curves of tomato crops grown in a semi-commercial greenhouse. Upon comparing the induction curves of 20 tomato crops planted on a north-south lane in the greenhouse, we found two plants with unique induction curve patterns. One of these two plants showed visible symptoms of physiological dysfunction 7 days after measurement. Thus, our chlorophyll fluorescence imaging system appears to be a useful tool for plant health monitoring in horticultural production.
A complex interaction involving temporal as well as spatial factors influences plant growth and development. To optimally control the environment for plant growth, an important first step is to develop crop growth models that can predict daily plant growth based on weather data, management practices, and plant genetic information. When this is accomplished, sensing data can contribute to enhanced accuracy of the plant growth model. Recent efforts to integrate plant growth models with sensing methods have provided an opportunity to optimize future plant production systems. In other words, good results require feedback from plants. The general concept is coined the ‘speaking plant’ concept. Consequently, sensors are an essential part of control systems. Machine vision can provide information about current crop status, including growth, nutrient stress and pest infestation. In this paper, current technologies are introduced and sensing system using artificial intelligence are described.