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Plant Image Analysis Machine Vision System in Greenhouse

Abstract : In this paper, we use greenhouse field plants as studying objects to build a plant image analysis agricultural intelligent machine vision system based on web control. The system can provide data support for intelligent decision when manage a producing process by acquiring agronomic parameters of plant development and plant nutrition in real-time and remote through image analysis algorithm and relevant hardware and software platforms. For the software part of the system, we use an assembled installing environment based on Windows, Apache, PHP, MySql as the web application platform and establish the data structure of B/S network model based on Web. We build an image data base and use the agricultural parameters of plant development and plant nutrition to analyze the image and dynamically publish it on Web. At present, we have measurement algorithm modules include segmentation algorithm, shape recognition algorithm, 3D reconstruction algorithm, ranging algorithm, chlorophyll and nitrogen contents measure algorithm. We use these algorithms to obtain agricultural parameters such as nutrition, developing size, quality, diseases and pests and put thoroughly monitors and alerts into practice. For the hardware part of the system, it is consists of remote Web server, machine vision control equipments in field and sensors. The equipments on the platform can practice close-cycle control and condition management. These functions make it possible for the assemblage of internet controlled hardware system.
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Jianlun Wang, Xiaoying Cui, Dongbo Xu, Shuangshaung Zhao, Hao Liu, et al.. Plant Image Analysis Machine Vision System in Greenhouse. 8th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2014, Beijing, China. pp.662-690, ⟨10.1007/978-3-319-19620-6_74⟩. ⟨hal-01420285⟩

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