Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.
This article introduces a virtual private network (VPN) system deployed in a 70m2 operator station and greenhouse at Vietnam National University of Agriculture. The VPN system was not limited by geographical distance and allowed for remote monitoring of environmental parameters, viz. light (10-16000lux), soil moisture (20-100%), temperature (20-60oC), and ambient humidity (30-90%) with tolerances of ± 5% of the set/measured values. The signals from the sensor system were recorded with a sampling time of 6 seconds. The designed interface made it easy for Vietnamese users. The system was initially established, and tested successfully with Gerbera in the greenhouse. The VPN system allowed for remote programming, stable operation, and no loss of data during the signal collecting process, and allowed users to fully and quickly react when the system crashed or when the user needed to upgrade or maintain the greenhouse system.
The purpose of this study is to verify the feasibility of using an infrared camera to measure the surface temperature and then estimate the core body temperature of pigs under various infrared sources. We first conducted experiments with pig-body temperature measurement by an infrared camera. Then we have tried to increase the accuracy rate in estimating the core body temperature of pigs by measuring the temperature of moving pixels. We concluded that the relation between the core body temperature and the estimated pig-body temperature was y = 1.0392x−0.6621 with no static infrared source in the field view of infrared camera. With the existence of heat lamps in the field of view of the infrared camera, the relation was y = 1.0248x − 0.0921. With both the root mean square error and the mean absolute error lower than 1.12 ◦C, the experimental simulation results show that the proposed method is feasible and effective in fast and non-contact evaluation of pigbody temperature under various infrared sources.
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