Min-Sheng Liao

On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system

The system monitors both environmental factors and growth traits of Phalaenopsis.The system provides quantitative information with high spatiotemporal resolution.The system has been verified by long-term experiments.The relation between Phalaenopsis growth and environmental factors is revealed. Traditional methods for monitoring the environmental factors of a greenhouse and the growth of Phalaenopsis orchids often suffer from low spatiotemporal resolution, high labor-intensity, requiring much time, and a lack of automation and synchronization. To solve these problems, this study develops an Internet of Things (IoT)-based system to monitor the environmental factors of an orchid greenhouse and the growth status of Phalaenopsis at the same time. The whole system consists of an IoT-based environmental monitoring system and an IoT-based wireless imaging platform. An image processing algorithm based on the Canny edge detection method, the seeded region growing (SRG) method, and the mathematical morphology is also developed to estimate the leaf area of Phalaenopsis. The long-term experiments with respect to four different environmental conditions for cultivating Phalaenopsis are conducted. The statistical analysis methods, including the one-way ANOVA, two-way ANOVA, and Games-Howell test, are performed to examine the relation between the growth of Phalaenopsis leaves and the environmental factors in the greenhouse. The optimal cultivation conditions for Phalaenopsis can be easily identified. The experimental results indicate that the daily average growth rate of the leaf area of Phalaenopsis is approximately 79.41mm2/day when the temperature and relative humidity in the greenhouse are controlled at 28.832.58 (C) and 71.818.88 (%RH), respectively. The proposed system shows a great potential to provide quantitative information with high spatiotemporal resolution to floral farmers. It is promisingly expected that the proposed system will effectively contribute to updating farming strategies for Phalaenopsis in the future.

Adaptive Classification of Special Events in Agroecological Monitoring Systems for Pest Management

With powerful feasible integration of distributed sensing capability, real-time data analysis, and remote surveillance, wireless sensor networks (WSNs) provide a new insight into agroecological observation in ways of extending spatial and temporal scales. Through WSNs, some unexpected phenomena can be found, and new paradigms can be developed. Although employing the WSN technology can facilitate agroecological observation, one of the major challenges that need to be overcome is the abnormal readings caused by sensor failure, energy depletion of sensor nodes, low durability of protective cases in a wild environment, unreliable wireless communication, etc. In this study, a WSN-based ecological monitoring system is presented and practically deployed in a field to monitor the number of the oriental fruit fly (Bactrocera dorsalis (Hendel)) and capture long-term and up-to-minute natural environmental fluctuations. Moreover, an adaptive classification approach, built upon self-organizing maps and support vector machines, is incorporated into the monitoring system to automatically identify special events of pest outbreaks and sensor faults. Once the events are detected, farmers and government officials can take precautionary action in time before pest outbreaks cause an extensive loss or schedule maintenance tasks to repair monitoring devices. The proposed classification approach is easily adopted in different monitored farms, and it can automatically identify special events based on machine learning techniques without requiring additional manpower.

Toward a higher yield: a wireless sensor network-based temperature monitoring and fan-circulating system for precision cultivation in plant factories

Currently, global warming is worsening, causing the difficulty of cultivating crops in open fields, and leading to unstable quality of crops. Plant factories provide a well-controlled growth environment for precisely cultivating plants. However, uneven temperature distributions (UTDs) still occur at each cultivation shelf in plant factories, which decreases the yields (fresh weight) of plants. In this study, a wireless sensor network (WSN)-based automatic temperature monitoring and fan-circulating system for precision cultivation in plant factories is proposed, and it is built upon the technologies of WSN, ordinary kriging spatial interpolation, and automation control, to precisely find the UTD areas of cultivation shelves. Once a UTD area occurs, the fan-circulating system can be triggered immediately to automatically trace the area and circulate the air. This action can effectively improve the air flow in the cultivation zone, providing optimal growth conditions for plants. The proposed system has been deployed in two plant factories that grew Boston lettuces, and a series of performance evaluation experiments were conducted. The experimental results indicate that the fresh weight of the harvested lettuces increases by 61–109% when employing the proposed system that efficiently and significantly decreases the variation of the temperature in the cultivation zone.