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Noncontact canned compost maturity determining method

A non-contact, composting technology, applied in the field of agricultural informatics, can solve problems such as differences in surface shape characteristics

Inactive Publication Date: 2018-10-19
NANJING AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, there are some differences in the surface shape characteristics of decomposed fertilizers and undecomposed fertilizers

Method used

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  • Noncontact canned compost maturity determining method
  • Noncontact canned compost maturity determining method
  • Noncontact canned compost maturity determining method

Examples

Experimental program
Comparison scheme
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Embodiment Construction

[0063] The present invention will be further described below in conjunction with examples, but the protection scope of the present invention is not limited to this:

[0064] Take a composting plant as an example. The raw materials for composting are animal manure and tail vegetables. A camera and a thermal imager are installed on the top of the fermenter to monitor a production cycle of the fermenter. The data collection interval is set to 2 hours. A total of 1500 thermal imaging images and compost surface are collected. Article data. There are 300 test samples and 1200 training samples.

[0065] Combine figure 1 , A method for judging the maturity of non-contact canned compost, including the following steps:

[0066] S1. Extract image data at time t, including 255×3 dimensional thermal image color histogram data and compost surface RGB image data.

[0067] Install a camera with network communication function for shooting natural compost images and a thermal imager for shooting therm...

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PUM

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Abstract

The invention discloses a noncontact canned compost maturity determining method comprising the following steps: S1, extracting image data at time t; S2, preprocessing; S3, based on data obtained in S2, constructing a convolutional neural network (CNN) for extracting the feature of a compost image to obtains a 255-dimensional feature vector; S4, combining the color histogram data of an thermal image in the S1 with the feature vector output by the image feature extraction CNN in the S3 to form a real-time compost feature, and normalizing the real-time compost feature; S5 performing prediction based on a long short term memory (LSTM) network; and S6, outputting a determined result. The method for real-time detection of compost state based on temperature and appearance is accurate in results and easy to operate.

Description

Technical field [0001] The invention uses the natural image information of the compost pile and the thermal imaging image taken by the thermal imager to perform feature extraction and then classification through a deep learning neural network to determine the maturity of the compost in real time, and belongs to the field of agricultural informatics. Background technique [0002] The maturity of fertilizers is extremely important for the growth of crops. The use of immature fertilizers will not only not have a positive effect on the growth of crops, but will also attract flies and worms to lay eggs and corrode the roots of crops. The proliferation of microorganisms will also cause soil hypoxia. . The fully decomposed fertilizer will not cause adverse effects on the environment and is convenient for transportation, which is beneficial to improve soil fertility and promote plant growth. [0003] The maturity is an index that reflects the degree of stabilization of the composting proc...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/44G06T7/90
CPCG06T7/0002G06T2207/10024G06T2207/20032G06T2207/20081G06T2207/20084G06T2207/30188G06T7/44G06T7/90
Inventor 薛卫胡雪娇徐阳春韦中梅新兰陈行健
Owner NANJING AGRICULTURAL UNIVERSITY
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