Machine vision based pre-warning system and method for pest and disease damage

A technology of machine vision and early warning system, which is used in devices, applications, animal husbandry, etc. to capture or kill insects to achieve the effect of improving timeliness and accuracy

Inactive Publication Date: 2016-08-17
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current advanced greenhouse computer monitoring and early warning system

Method used

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  • Machine vision based pre-warning system and method for pest and disease damage
  • Machine vision based pre-warning system and method for pest and disease damage
  • Machine vision based pre-warning system and method for pest and disease damage

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] visit figure 1 ~~ Figure 7 , the machine vision-based pest and disease early warning system and method, is characterized in that it includes a Canon EOS7D camera (1) installed at a fixed position in the corner of the greenhouse, used for image data collection of crops in the greenhouse, and integrated sensors for environmental parameters (2) installed in The top of the greenhouse is used for data collection of various environmental parameters in the greenhouse, and the image processing system (3) is located in the central control room of the greenhouse, which is used to process and record the image data and environmental parameter data collected in the greenhouse. Canon EOS7D camera (1) and environmental parameter integrated sensor (2) are connected to the image processing system;

Embodiment 2

[0047] Embodiment 2: This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0048] The main parameters of the Canon EOS7D camera (1) are: 18 million effective pixels; actual focal length: f=18-135mm; file format: JPEG, RAW (14 bits), and can record RAW+JPEG at the same time.

[0049] The environmental parameter integrated sensor (2) is composed of various environmental parameter sensors such as temperature, humidity, and light intensity; it is used to measure various environmental parameters in the greenhouse, and is transmitted to the image processing system (3) using a wireless network for Basis for alarm source warning.

[0050] The image processing system (3) specifically includes:

[0051] The alarm source early warning module (3-1) acquires environmental parameters according to various environmental sensors on site, and matches the real-time environmental parameters with the suitable growth environment parameters of crops to gi...

Embodiment 3

[0055] This machine vision-based early warning system and method for pests and diseases uses the above-mentioned system to operate, and is characterized in that the operation steps are as follows:

[0056] Step 1: Canon EOS7D camera (1) and environmental parameter integrated sensor (2) collect data every 3 hours, and transmit the data to the image processing system (3) through wireless network.

[0057] Step 2: In the image processing system (3), the alarm source warning module (3-1) is started, and the environmental parameter data collected on the spot is matched with the preset expert experience value to judge whether it is within a reasonable range.

[0058] Step 3: If the environmental parameter data is not within a reasonable range, start the warning sign and early warning module (3-2). Image processing is performed on the image data collected by the Canon EOS7D camera (1) to further identify whether there are pests and diseases, and the types of pests and diseases. And ...

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Abstract

The invention provides a machine vision technology based pre-warning system and method for pest and disease damage of crops in greenhouses. According to the system, image monitoring data and environment parameters under the natural background are acquired by an in-situ Canon EOS7D camera image sensor and multiple environmental sensors, a dual pest and disease damage pre-warning system realizing warning source pre-warning and warning sign pre-warning is constructed, and meanwhile, an information processing function that a user can inquire pre-warning records at any time is realized. Real-time environmental parameters and environmental parameters suitable for crop growth are matched firstly to provide preliminary pre-warning, real-time image data are processed through filtering, segmentation, feature extraction and identification and classification with an image processing technology in machine vision, so that tomato early blight and leaf mold are automatically identified and classified, the pest and disease damage morbidity situation of crops at present is provided, and decision support and historical information record inquiry functions are supplied to the user. Manpower and material resources are greatly reduced, and meanwhile, the pest and disease damage management efficiency of the crops in the greenhouse is further improved.

Description

technical field [0001] The invention relates to a disease and insect pest early warning system, in particular to a machine vision-based disease and insect pest early warning system and method. Background technique [0002] Simultaneously promoting agricultural modernization while industrialization, informatization, and urbanization are in-depth development is a major task during the "Twelfth Five-Year Plan" period. The "Twelfth Five-Year Plan" is a critical period for building a well-off society in an all-round way, a critical period for deepening reform and opening up, accelerating the transformation of economic development methods, and an important period of opportunity for accelerating the development of modern agriculture. [0003] Digital agriculture and precision operations are the direction and requirements of modern agricultural development. In terms of crop disease and insect pest analysis, digital agriculture requires fast and accurate acquisition of plant disease...

Claims

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Application Information

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IPC IPC(8): A01M1/02
CPCA01M1/026
Inventor 李昕檀新瑞柴宇燊
Owner SHANGHAI UNIV
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