Field agricultural production scene perceptual computing method and device
A computing device and situation-aware technology, applied in computing, instruments, data processing applications, etc., can solve problems that affect auxiliary decision-making in production management and cannot provide data support and basis
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Embodiment 1
[0032] The embodiment of the present invention provides a situation-aware calculation method for field agricultural production, see figure 1 , the method includes:
[0033] Step S1. Collect binocular image data and growth environment data of crops. The binocular stereo vision system is used to shoot binocular images of crops, and the image data of crops are collected; at the same time, the meteorological moisture monitoring system is used to monitor the growth environment data of crops, including indicators such as light, temperature, moisture, and soil parameters;
[0034] Step S2. According to the collected binocular image data, carry out point cloud data processing, and calculate the plant height and plant row spacing of the crop;
[0035] Step S3. According to the collected binocular image data, digital image processing is performed to calculate the canopy closure and growth information of the crop;
[0036] Step S4. The calculated plant height, plant-row spacing, canopy...
Embodiment 2
[0059] The embodiment of the present invention provides another method for calculating the situation of field agricultural production, see figure 2 , the method includes:
[0060] Step 201. Collect binocular image data and growth environment data of the crop. The binocular stereo vision system is used to shoot binocular images of crops, and the image data of crops are collected. Use the monitoring data of field production environment, including parameters such as light, temperature, moisture, soil, etc.;
[0061] Step 202. The collected crop growth environment data is combined with the crop growth model to calculate the growth state of the main organs in the crop growth process, and the morphological and structural parameters of each main organ, such as node length, leaf length, and plant height; taking corn as an example, Typical maize growth models include the relationship model between the length of the expanded leaf (MLDL) and the phyllotaxis (N), the relationship model...
Embodiment 3
[0068] Embodiments of the present invention provide a context-aware computing device for field agricultural production, see figure 2 , the device includes:
[0069] The data collection module 301 is used to collect binocular image data and growth environment data of crops.
[0070] The plant height and plant row spacing analysis module 302 is used to calculate the plant height and plant row spacing of the crop.
[0071] The canopy closure and growth information analysis module 303 is used to calculate the canopy closure and growth information of the crop;
[0072] The growth model optimization module 304 is used to calculate the obtained plant height, plant-row spacing, canopy canopy closure and growth as the morphological and structural indicators of the crop, and to optimize the crop growth model in real time;
[0073] The growth state module 305 is configured to calculate the growth state of the crop organs according to the growth environment data and in combination with...
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