基于综合指标品质评价算法的单色水果生长状态检测互联网架构

doi:10.13229/j.cnki.jdxbgxb20180910

Abstract

Abstract:

To solve the problem of quality detection of large-scale fruit crops, this paper proposes an Internet architecture for monochrome fruit growth status detection based on comprehensive index quality evaluation algorithm. Through the RGB color model, convolution exploration method and Gabor filtering, the color depth, size and appearance damage degree of the fruit were analyzed. The three results were comprehensively analyzed, and the fruit quality was divided into 6 grades for the growing fruit quality evaluation. The algorithm was integrated into the Internet architecture. Based on the traditional industrial system structure, the network interconnection, data intelligence and security between the system and the outside world were improved. The structural optimization of the fruit growth detection Internet architecture was realized, and the structure flat development was mad and structure network, data interconnection, security level integration were promoted, which was used in real-time monitoring of crops and recording the fruit growth process. Experiments conducted by common monochrome fruits in Changchun show that the algorithms in this architecture can formulate different grading evaluation standards according to specific types, improve the interaction ability and quality evaluation efficiency between the system and the outside world, and reduce manual input.

Key words: agricultural engineering, comprehensive indicator quality evaluation, growth detection, internet architecture, RGB color model, convolution exploration, Gabor filtering

CLC Number:

TP393

En-ze LIU,Wen-fu WU. Monochrome fruit growth detection internet architecture based oncomprehensive indicator quality evaluation algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2019-2026.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Quality grading standards"

等级	分档	符合的要求
1	上	$s g n (F - F 6) + s g n (T - T 6) + s g n (D - D 6) = 3$
	中	$s g n (F - F 6) + s g n (T - T 6) + s g n (D - D 6) = 2$
	下	$s g n (F - F 6) + s g n (T - T 6) + s g n (D - D 6) = 1$
2	上	$s g n (F - F 5) + s g n (T - T 5) + s g n (D - D 5) = 3$
	中	$s g n (F - F 5) + s g n (T - T 5) + s g n (D - D 5) = 2$
	下	$s g n (F - F 5) + s g n (T - T 5) + s g n (D - D 5) = 1$
3	上	$s g n (F - F 4) + s g n (T - T 4) + s g n (D - D 4) = 3$
	中	$s g n (F - F 4) + s g n (T - T 4) + s g n (D - D 4) = 2$
	下	$s g n (F - F 4) + s g n (T - T 4) + s g n (D - D 4) = 1$
4	上	$s g n (F - F 3) + s g n (T - T 3) + s g n (D - D 3) = 3$
	中	$s g n (F - F 3) + s g n (T - T 3) + s g n (D - D 3) = 2$
	下	$s g n (F - F 3) + s g n (T - T 3) + s g n (D - D 3) = 1$
5	上	$s g n (F - F 2) + s g n (T - T 2) + s g n (D - D 2) = 3$
	中	$s g n (F - F 2) + s g n (T - T 2) + s g n (D - D 2) = 2$
	下	$s g n (F - F 2) + s g n (T - T 2) + s g n (D - D 2) = 1$
6	上	$s g n (F - F 1) + s g n (T - T 1) + s g n (D - D 1) = 3$
	中	$s g n (F - F 1) + s g n (T - T 1) + s g n (D - D 1) = 2$
	下	$s g n (F - F 1) + s g n (T - T 1) + s g n (D - D 1) = 1$

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Table 3

Table 4

Fig.6

Table 5

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分类	本文算法			文献[14]方法
分类	识别数	正确数	正确率/%	识别数	正确数	正确率/%
优质品	996	995	99.7	984	980	95.7
一般品	1000	998		971	951
残次品	998	998		964	942

分类	本文算法			文献[15]方法
分类	识别数	正确数	正确率/%	识别数	正确数	正确率/%
优质品	999	999	99.6	985	982	95.8
一般品	996	996		962	953
残次品	995	994		945	939

分类	本文算法			文献[16]方法
分类	识别数	正确数	正确率/%	识别数	正确数	正确率/%
优质品	992	990	99.4	975	953	95.1
一般品	997	995		952	942
残次品	998	998		968	956

消耗物		工业互联网	人工
化肥	质量/kg	1 772	1 749
化肥	价值/元	8 442	8 345
耗电量	电量/（kW·h）	1 421	1 054
耗电量	价值/元	852	612
农作物	价值/元	13 000	13 000
设备费用	价值/元	20 000	13 000
人工费	价值/元	7 000	50 000
场地费	价值/元	4 000	5 000
总成本	价值/元	53 294	89 957

Monochrome fruit growth detection internet architecture based oncomprehensive indicator quality evaluation algorithm

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