基于颜色矩的土豆、玉米、苹果叶片病害异常检测

doi:10.3969/j.issn.1004-1524.2022.10.17

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (10): 2230-2239.DOI: 10.3969/j.issn.1004-1524.2022.10.17

基于颜色矩的土豆、玉米、苹果叶片病害异常检测

张梓婷¹(), 韩金玉¹, 张东辉¹, 李晗¹, 李铭源¹, 邓志平², 孙晓勇¹^,^*()

1.山东农业大学信息科学与工程学院,山东泰安 271000
2.浙江省农业科学院病毒学与生物技术研究所,浙江杭州310021

收稿日期:2021-08-07 出版日期:2022-10-25 发布日期:2022-10-26
通讯作者: 孙晓勇
作者简介:*孙晓勇,E-mail: johnsunx1@126.com
张梓婷(1998—),女,安徽宿州人,硕士研究生,主要从事大数据与人工智能研究。E-mail: tingzhzi@163.com
基金资助:
国家自然科学基金(32070684)

Anomaly detection of potato, maize and apple leaf diseases based on color moments

ZHANG Ziting¹(), HAN Jinyu¹, ZHANG Donghui¹, LI Han¹, LI Mingyuan¹, DENG Zhiping², SUN Xiaoyong¹^,^*()

1. College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, Shandong, China
2. Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China

Received:2021-08-07 Online:2022-10-25 Published:2022-10-26
Contact: SUN Xiaoyong

摘要/Abstract

摘要：

农作物病害是影响粮食产量的重要因素之一。目前,大部分研究以已知病害作为数据来源,使用传统机器学习和深度学习方法进行病害识别与分类,这种模型构建方法需要大量的病害数据,而当新发病害出现时,很可能因为检测不到而错过最佳预警时间。为解决该问题,本文拟提出一种仅使用正常农作物叶片数据集作为训练数据便可检测出叶片病害异常的方法。具体地,本研究提出一种基于k-means++聚类与图像分块的农作物叶片病害异常检测方法,通过图像去噪、图像分割、图像截取等预处理操作后,提取图像的颜色矩特征,对训练集进行k-means++聚类,构建比对模型并设置阈值,从而确定测试集异常与否。试验使用的土豆、玉米与苹果数据集均下载于Kaggle网站。通过调整聚类数与分块数,在土豆、玉米和苹果数据集上,识别准确率分别达到了89%、95%、95%以上,并且在玉米和苹果两种数据集上的漏警率为0。

关键词: 叶片病害, 异常检测, 图像分块

Abstract:

Crop diseases are one of the important factors on crop yield. At present, most researches use traditional machine learning and deep learning methods to identify and classify known diseases. However, this kind of model requires a lot of disease data, and it is not suitable for detection of new diseases. To overcome this problem, this work aims to detect the abnormal leaf diseases by using only the healthy leaf data sets as the training data. Specifically, a method of anomaly detection of crop leaf diseases based on k-means++clustering and image blocking is proposed. After image denoising, segmentation, interception and other preprocessing operations, the color moment features of the image are extracted, k-means++clustering is performed on the training set, and the comparison models are constructed and the threshold is set to determine whether the test set is abnormal or not. The datasets of potato, maize and apple leaves used in this experiment are downloaded from Kaggle website. By adjusting the cluster number and block number, the accuracy rate of potato, maize and apple leaf on test set is higher than 89%, 95% and 95%, respectively, and the missing alarm rates on maize and apple are 0.

Key words: leaf diseases, anomaly detection, image blocking

中图分类号:

S431.9
TP391

张梓婷, 韩金玉, 张东辉, 李晗, 李铭源, 邓志平, 孙晓勇. 基于颜色矩的土豆、玉米、苹果叶片病害异常检测[J]. 浙江农业学报, 2022, 34(10): 2230-2239.

ZHANG Ziting, HAN Jinyu, ZHANG Donghui, LI Han, LI Mingyuan, DENG Zhiping, SUN Xiaoyong. Anomaly detection of potato, maize and apple leaf diseases based on color moments[J]. Acta Agriculturae Zhejiangensis, 2022, 34(10): 2230-2239.

图/表 16

参考文献 24

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植物种类 Plant species	样本情况 Sample status		样本数量 Sample quantity
土豆Potato	正常Normal	健康Healthy	152
	异常Abnormal	早疫病Early blight	1 000
		晚疫病Late blight	1 000
玉米Maize	正常Normal	健康Healthy	1 162
	异常Abnormal	普通锈病Common rust	1 142
		灰斑病Gray spot	463
		叶枯病Leaf blight	935
苹果Apple	正常Normal	健康Healthy	1 638
	异常Abnormal	黑腐病Black rot	621
		雪松锈病Cedar rust	275
		黑星病Black-spot	630

植物种类 Plant species	样本情况 Sample status		样本数量 Sample quantity
土豆Potato	正常Normal	健康Healthy	152
	异常Abnormal	早疫病Early blight	1 000
		晚疫病Late blight	1 000
玉米Maize	正常Normal	健康Healthy	1 162
	异常Abnormal	普通锈病Common rust	1 142
		灰斑病Gray spot	463
		叶枯病Leaf blight	935
苹果Apple	正常Normal	健康Healthy	1 638
	异常Abnormal	黑腐病Black rot	621
		雪松锈病Cedar rust	275
		黑星病Black-spot	630

植物种类 Plant species	数据集类型 Dataset type	正常样本数 Quantity of normal samples	异常样本数 Quantity of abnormal samples
土豆Potato	训练集Training set	120	0
	测试集Testing set	30	20
玉米Maize	训练集Training set	928	0
	测试集Testing set	232	150
苹果Apple	训练集Training set	1 232	0
	测试集Testing set	308	211

植物种类 Plant species	数据集类型 Dataset type	正常样本数 Quantity of normal samples	异常样本数 Quantity of abnormal samples
土豆Potato	训练集Training set	120	0
	测试集Testing set	30	20
玉米Maize	训练集Training set	928	0
	测试集Testing set	232	150
苹果Apple	训练集Training set	1 232	0
	测试集Testing set	308	211

颜色矩 Color moment	聚类数 Cluster number	A		MAR
颜色矩 Color moment	聚类数 Cluster number	1×1	2×2	1×1	2×2
RGB	0	80.8	67.2	43.0	82.0
	2	85.2	82.0	13.0	38.0
	3	74.4	81.2	21.0	34.0
HSV	0	64.4	67.2	31.0	79.0
	2	72.0	66.8	45.0	80.0
	3	72.0	67.2	54.0	75.0

基于颜色矩的土豆、玉米、苹果叶片病害异常检测

Anomaly detection of potato, maize and apple leaf diseases based on color moments

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参考文献 24

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聚类数 Cluster number	A		MAR
聚类数 Cluster number	1×1	2×2	1×1	2×2
0	82.8	74.8	18.0	61.0
2	89.2	78.8	7.0	22.0
3	80.0	82.8	14.0	37.0

模型 Model	A			MAR
模型 Model	土豆Potato	玉米Maize	苹果Apple	土豆Potato	玉米Maize	苹果Apple
KNN	92.0	89.8	79.2	5.0	10.4	36.8
ABOD	92.4	92.2	75.4	3.0	4.2	47.4
HBOS	76.4	85.6	60.8	48.0	20.0	80.6
COF	79.6	59.0	58.8	47.0	89.6	89.2
CBLOF	90.8	81.6	79.6	4.0	29.4	33.6
Iforest	81.2	83.0	62.4	31.0	26.8	77.2
CMAD	89.2	98.7	96.4	7.0	0	0

[1]	张红涛, 李艺嘉, 谭联, 许帅涛. 基于CS-SVM的谷子叶片病害图像识别[J]. 浙江农业学报, 2020, 32(2): 274-282.
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颜色矩 Color moment	聚类数 Cluster number	A		MAR
颜色矩 Color moment	聚类数 Cluster number	1×1	2×2	1×1	2×2
RGB	0	60.7	60.7	100.0	100.0
	2	68.1	77.1	44.9	40.9
	3	68.6	73.8	38.7	46.0
HSV	0	80.9	83.3	1.2	1.2
	2	87.7	84.2	24.8	37.0
	3	89.7	92.3	8.0	11.0

颜色矩 Color moment	聚类数 Cluster number	A		MAR
颜色矩 Color moment	聚类数 Cluster number	1×1	2×2	1×1	2×2
RGB	0	60.7	60.7	100.0	100.0
	2	68.1	77.1	44.9	40.9
	3	68.6	73.8	38.7	46.0
HSV	0	80.9	83.3	1.2	1.2
	2	87.7	84.2	24.8	37.0
	3	89.7	92.3	8.0	11.0

颜色矩 Color moment	聚类数 Cluster number	A		MAR
颜色矩 Color moment	聚类数 Cluster number	1×1	2×2	1×1	2×2
RGB	0	59.3	59.5	100	100.0
	2	57.9	58.7	63.8	91.1
	3	60.2	65.7	42.2	42.2
HSV	0	54.3	69.7	6.4	27.6
	2	71.4	69.4	49.1	71.0
	3	76.2	70.0	46.7	60.6

颜色矩 Color moment	聚类数 Cluster number	A		MAR
颜色矩 Color moment	聚类数 Cluster number	1×1	2×2	1×1	2×2
RGB	0	59.3	59.5	100	100.0
	2	57.9	58.7	63.8	91.1
	3	60.2	65.7	42.2	42.2
HSV	0	54.3	69.7	6.4	27.6
	2	71.4	69.4	49.1	71.0
	3	76.2	70.0	46.7	60.6