深度学习在作物及其近缘种抗病性智能鉴定上的研究进展

doi:10.3969/j.issn.1004-1524.20236105

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (8): 1993-2012.DOI: 10.3969/j.issn.1004-1524.20236105

深度学习在作物及其近缘种抗病性智能鉴定上的研究进展

潘攀¹^,²(), 张建华¹^,²^,^*(), 郑晓明²^,³, 周国民²^,⁴, 胡林¹^,², 冯全⁵, 柴秀娟¹^,²

1.中国农业科学院农业信息研究所,北京 100081
2.中国农业科学院国家南繁研究院,海南三亚 572024
3.中国农业科学院作物科学研究所,北京 100081
4.中国农业科学院农田灌溉研究所,河南新乡 453002
5.甘肃农业大学机电工程学院,甘肃兰州 730070

收稿日期:2023-02-05 出版日期:2023-08-25 发布日期:2023-08-29
作者简介:潘攀(1997—),男,浙江台州人,硕士研究生,研究方向为视觉智能感知。E-mail:82101225584@caas.cn
通讯作者: *张建华,E-mail:zhangjianhua@caas.cn
基金资助:
国家重点研发计划(2022YFF0711805);国家自然科学基金(31971792);国家自然科学基金(32160421);中国农业科学院创新工程(CAAS-ASTIP-2016-AII);中国农业科学院创新工程(CAAS-ASTIP-2023-AII);中央级公益性科研院所基本科研业务费专项(Y2022XK24);中央级公益性科研院所基本科研业务费专项(Y2022QC17);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2022-14);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2023-06);三亚中国农业科学院国家南繁研究院南繁专项(YDLH01);三亚中国农业科学院国家南繁研究院南繁专项(YDLH07);三亚中国农业科学院国家南繁研究院南繁专项(YBXM10);三亚中国农业科学院国家南繁研究院南繁专项(ZDXM23011);三亚中国农业科学院国家南繁研究院南繁专项(YBXM2312)

Research progress of deep learning in intelligent identification of disease resistance of crops and their related species

PAN Pan¹^,²(), ZHANG Jianhua¹^,²^,^*(), ZHENG Xiaoming²^,³, ZHOU Guomin²^,⁴, HU Lin¹^,², FENG Quan⁵, CHAI Xiujuan¹^,²

1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. National Nanfan Research Institute, Chinese Academy of Agricultural Sciences, Sanya 572024, Hainan, China
3. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, Henan, China
5. School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China

Received:2023-02-05 Online:2023-08-25 Published:2023-08-29

摘要/Abstract

摘要：

作物及其近缘种抗病性的精准鉴定是筛选和培育作物抗病性品种的关键环节,是安全有效防治作物病害的重要方式,也是野生种质资源异位保存和开发利用的重要基础。传统的抗病性鉴定方法工作量巨大,且严重依赖于调查人员的主观判断,快速、准确的作物及其近缘种抗病性智能化鉴定方式是未来的发展方向。近年来,随着深度学习方法的快速发展与大量应用,基于深度学习的作物及其近缘种抗病性智能鉴定成为可能。本文首先以水稻3大病害(稻瘟病、白叶枯病、纹枯病)为例,从阐述其抗病性鉴定的规范标准和传统抗病性鉴定方法出发,随后从病害检测、病害分割和病害危害程度评估3方面综述了深度学习在作物及其近缘种抗病性智能鉴定中的研究进展,凝练了深度学习在抗病性鉴定上的应用情况和面临的难点与挑战,并对未来进一步研究的方向与发展趋势进行展望,旨在为深度学习在作物及其近缘种抗病性鉴定中的进一步研究应用提供参考。

关键词: 作物, 近缘种, 种质资源鉴定, 抗病性, 智能鉴定, 精准鉴定, 深度学习

Abstract:

Accurate identification of disease resistance of crops and their related species is the critical link in screening and breeding disease resistant varieties of crops, an important way to safely and effectively control crop diseases, and an essential basis for the ex-situ conservation and exploitation of wild germplasm resources. The traditional disease resistance identification methods rely heavily on the subjective judgment of investigators, which is time-consuming and laborious.Rapid, accurate and intelligent identification of disease resistance of crops and their related species is in great need. In recent years, with the rapid development and application of deep learning methods, the intelligent identification of disease resistance of crops and their related species based on deep learning has become possible. In the present assay, we firstly introduced the traditional disease resistance identification methods and standards by taking three main diseases in rice (rice blast, rice leaf blight and rice sheath blight) as an example, then reviewed the progress of deep learning in intelligent identification of disease resistance in crops and their related species from three aspects of disease detection, disease segmentation, and diseases index assessment. Finally, we summarized difficulties and challenges in the application of deep learning in disease resistance identification, and discussed the feasibility and development trend to provide references for further research on deep learning in disease resistance identification of crops and their related species.

Key words: crops, related species, identification of germplasm resources, disease resistance, intelligent identification, accurate identification, deep learning

中图分类号:

S126

潘攀, 张建华, 郑晓明, 周国民, 胡林, 冯全, 柴秀娟. 深度学习在作物及其近缘种抗病性智能鉴定上的研究进展[J]. 浙江农业学报, 2023, 35(8): 1993-2012.

PAN Pan, ZHANG Jianhua, ZHENG Xiaoming, ZHOU Guomin, HU Lin, FENG Quan, CHAI Xiujuan. Research progress of deep learning in intelligent identification of disease resistance of crops and their related species[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1993-2012.

图/表 8

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编号 No.	文献 Reference	年份 Year	物种 Species	病害 Disease	数据集获取方式 Datasets obtaining method	数据集图像数量 Image quaity in datase	数据增强后的图像数量 Image quantity after data augmentation	检测网络框架 Framework of detection network	平均检测时间 Average detection time	平均精度 Average precision (mAP)
1	Zhou等^[27] Zhou et al.^[27]	2019	水稻 Rice	白叶枯病、稻瘟病、纹枯病 Rice leaf blight, rice blast, rice sheath blight	田间采集 Field collection	3 010	7 448	Faster R-CNN	稻瘟病0.65 s,白叶枯病 0.82 s,纹枯病0.53 s Rice blast 0.65 s, rice leaf blight 0.82 s, rice sheath blight 0.53 s	97.20%
2	Li等^[26] Li et al.^[26]	2020	水稻 Rice	纹枯病、胡麻斑病 Rice sheath blight, rice brown spot	田间采集 Field collection	5 320	—	Faster R-CNN	—	—
3	Masood等^[43] Masood et al.^[43]	2020	水稻 Rice	—	田间采集 Field collection	1 700	—	Mask R-CNN	—	87.60%
4	Sethy等^[24] Sethy et al.^[24]	2020	水稻 Rice	稻曲病 Rice false smut	田间采集 Field collection	50	—	Faster R-CNN	—	—
5	Agbulos等^[36] Agbulos et al. ^[36]	2021	水稻 Rice	稻瘟病、胡麻斑病 Rice blast, rice brown spot	Kaggle	200	—	YOLOv3	—	73.33%
6	Anandhan等^[44] Anandhan et al.^[44]	2021	水稻 Rice	胡麻斑病、稻瘟病、纹枯病 Rice brown spot, rice blast, rice sheath blight	田间采集 Field collection	1 500	—	Mask R-CNN	—	稻瘟病96.00%,胡麻斑病95.00%,纹枯病94.50% Rice blast 96.00%, rice brown spot 95.00%, rice sheath blight 94.50%
7	Bari等^[25] Bari et al.^[25]	2021	水稻 Rice	胡麻斑病、稻瘟病 Rice brown spot, rice blast	田间采集、Kaggle Field collection, Kaggle	2 400	16 800	Faster R-CNN	—	健康叶片99.25%,稻瘟病 98.09%,胡麻斑病 98.85% Healthy leaves 99.25%, rice blast 98.09%, rice brown spot 98.85%
8	Jhatial等^[35] Jhatial et al.^[35]	2022	水稻 Rice	白叶枯病、稻瘟病、东格鲁病毒病、胡麻斑病 Rice leaf blight, rice blast, rice tungro spherical virus disease, rice brown spot	Kaggle	400	—	YOLOv5	—	62.00%
9	江鹏等^[45] Jiang et al.^[45]	2019	苹果 Apple	褐斑病、花叶病、铁锈病 Apple brown spot, apple mosaic, apple rust	田间采集 Field collection	1 248	—	SSD	—	79.63%
10	Sardoğan等^[29] Sardoğan et al.^[29]	2020	苹果 Apple	黑斑病 Black spot	田间采集 Field collection	700	—	Faster R-CNN	—	84.50%
11	邸洁等^[46] Di et al.^[46]	2020	苹果 Apple	黑星病、黑腐病、雪松锈病 Apple scab, apple rot, cedar apple rust	PlantVillage	1 404	—	Tiny-YOLO	3.57 ms	99.86%
12	李鑫然等^[34] Li et al.^[34]	2021	苹果 Apple	斑点落叶病、褐斑病、花叶病、灰斑病、锈病 Alternaria leaf spot, brown spot, apple mosaic, gray spot, apple rust	PlantVillage、田间采集 PlantVillage, Field collection	2 029	20 050	Faster R-CNN	0.208 s	82.48%
13	Di等^[39] Di et al.^[39]	2022	苹果 Apple	黑星病、黑腐病、雪松锈病 Apple scab, apple rot, cedar apple rust	PlantVillage	1 404	—	Tiny-YOLO	3.57 ms	99.99%
14	Li等^[47] Li et al.^[47]	2022	苹果 Apple	苹果斑点落叶病,等 Alternaria leaf spot, etc	田间采集 Field collection	1 587	24 757	YOLOv5	29.4 ms	96.04%
15	王远志等^[31] Wang et al.^[31]	2022	苹果 Apple	雪松锈病、灰斑病、黑星病 Cedar apple rust, gray spot, apple scab	PlantVillage	730	3 879	Faster R-CNN	0.201 s	87.21%
16	李就好等^[33] Li et al.^[33]	2020	苦瓜 Bitter melon	白粉病、灰斑病、蔓枯病、斑点病 Powdery mildew, gray leaf spot, gummy stem blight, phyllosticta leaf spot	田间采集、 ImageNet Field collection, ImageNet	1 204	10 627	Faster R-CNN	0.322 s	86.39%
17	刘延鑫等^[48] Liu et al.^[48]	2022	烟草 Tobacco	普通花叶病、黄瓜花叶病毒病、赤星病、烟草野火病、气候性斑点病 Ccommon mosaic disease, cucumber mosaic virus disease, scab disease, tobacco wildfire disease, climatic spot disease	田间采集 Field collection	1 750	4 500	YOLOv3	0.31 s	77.00%
18	刘阗宇等^[49] LIU et al.^[49]	2018	葡萄 Grape	褐斑病、白粉病、灰霉病、霜霉病、黑痘病、炭疽病 Brown spot, powdery mildew, botrytis, downy mildew, black pox, anthracnose	田间采集 Field collection	6 000	—	Faster R-CNN	1.416 s	66.47%
19	樊湘鹏等^[50] Fan et al.^[50]	2021	葡萄 Grape	白粉病、霜霉病、黑霉病、花叶病毒病、褐斑病 Powdery mildew, downy mildew, black mould blight, mosaic virus disease, brown spot	田间采集 Field collection	1 990	19 900	CNN	0.327 s	98.02%
20	Wang等^[37] Wang et al.^[37]	2021	番茄 Tomato	早疫病、晚疫病、番茄黄化卷叶病毒病、褐斑病,等 Early blight, late blight, yellow leaf curl virus, brown spot, etc	田间采集、互联网 PlantVillage, Internet	15 000	—	YOLOv3	20.28 ms	96.41%
21	陶国柱等^[51] Tao et al.^[51]	2021	番茄 Tomato	细菌性斑点病、早疫病、晚疫病、病毒病、叶霉病、白粉病 Bacterial leaf spot, early blight, late blight, virus disease, leaf mould, powdery mildew	PlantVillage等互联网渠道 Internet channels such as PlantVillage	4 529	5 574	YOLOv4	13.3 ms	86.23%
22	Islam等^[52] Islam et al.^[52]	2019	马铃薯 Potato	早疫病、晚疫病 Early blight, late blight	PlantVillage	2 152	—	CNN	—	99.43%
23	赵越等^[28] Zhao et al.^[28]	2022	马铃薯 Potato	早疫病、晚疫病 Early blight, late blight	Kaggle	—	8 109	Faster R-CNN	—	99.50%
24	Zhang等^[32] Zhang et al.^[32]	2021	大豆 Soybean	病毒菌、灰斑病、细菌性斑点病 Virus disease, frogeye leaf spot, bacterial spot	田间采集 Field collection	—	2 230	Faster R-CNN	—	83.34%
25	Wang等^[38] Wang et al.^[38]	2022	花生 Peanut	花生褐斑病、花生锈斑病 Rrown spot, peanut rust	PlantVillage、田间采集 PlantVillage, field collection	36 258(Plant Village) +4 631(田间采集) 36 258 (Plant Village)+4 631 (field collection)	—	YOLOv5	15 ms	93.73%
26	Li等^[53] Li et al.^[53]	2022	黄麻 Jute	茎腐病、炭疽病、黑带病、软腐病、尖枯病、枯萎病、黄麻花叶病、黄麻黄化病 Stem rot, anthracnose, black band, soft rot, tip blight, dieback, jute mosaic, jute chlorosis	田间采集 Field collection	4 418	—	YOLOv5	—	96.63%
27	Sert^[30] Sert^[30]	2021	马铃薯、辣椒 Potato, pepper	辣椒细菌性斑点病、马铃薯早疫病、马铃薯晚疫病 Pepper bacterial spot, potato early blight, potato late blight	PlantVillage 、田间采集 PlantVillage, Field collection	544(田间采集)+6 023 (PlantVillage) 544 (field collection)+ 6 023 (PlantVillage)	2 176(田间采集) 2176 (field collection)	Faster R_CNN	—	98.06%
28	Shill 等^[54] Shill et al.^[54]	2021	苹果、番茄等13种植物 13 plants including apple and tomato	苹果黑腐病等17种 17 diseases such as apple rot	PlantDoc	2 598	—	YOLOv4/YOLOv3	—	55.45%

编号 No.	文献 Reference	年份 Year	物种 Species	病害 Disease	数据集获取方式 Datasets obtaining method	数据集图像数量 Image quaity in datase	数据增强后的图像数量 Image quantity after data augmentation	检测网络框架 Framework of detection network	平均检测时间 Average detection time	平均精度 Average precision (mAP)
1	Zhou等^[27] Zhou et al.^[27]	2019	水稻 Rice	白叶枯病、稻瘟病、纹枯病 Rice leaf blight, rice blast, rice sheath blight	田间采集 Field collection	3 010	7 448	Faster R-CNN	稻瘟病0.65 s,白叶枯病 0.82 s,纹枯病0.53 s Rice blast 0.65 s, rice leaf blight 0.82 s, rice sheath blight 0.53 s	97.20%
2	Li等^[26] Li et al.^[26]	2020	水稻 Rice	纹枯病、胡麻斑病 Rice sheath blight, rice brown spot	田间采集 Field collection	5 320	—	Faster R-CNN	—	—
3	Masood等^[43] Masood et al.^[43]	2020	水稻 Rice	—	田间采集 Field collection	1 700	—	Mask R-CNN	—	87.60%
4	Sethy等^[24] Sethy et al.^[24]	2020	水稻 Rice	稻曲病 Rice false smut	田间采集 Field collection	50	—	Faster R-CNN	—	—
5	Agbulos等^[36] Agbulos et al. ^[36]	2021	水稻 Rice	稻瘟病、胡麻斑病 Rice blast, rice brown spot	Kaggle	200	—	YOLOv3	—	73.33%
6	Anandhan等^[44] Anandhan et al.^[44]	2021	水稻 Rice	胡麻斑病、稻瘟病、纹枯病 Rice brown spot, rice blast, rice sheath blight	田间采集 Field collection	1 500	—	Mask R-CNN	—	稻瘟病96.00%,胡麻斑病95.00%,纹枯病94.50% Rice blast 96.00%, rice brown spot 95.00%, rice sheath blight 94.50%
7	Bari等^[25] Bari et al.^[25]	2021	水稻 Rice	胡麻斑病、稻瘟病 Rice brown spot, rice blast	田间采集、Kaggle Field collection, Kaggle	2 400	16 800	Faster R-CNN	—	健康叶片99.25%,稻瘟病 98.09%,胡麻斑病 98.85% Healthy leaves 99.25%, rice blast 98.09%, rice brown spot 98.85%
8	Jhatial等^[35] Jhatial et al.^[35]	2022	水稻 Rice	白叶枯病、稻瘟病、东格鲁病毒病、胡麻斑病 Rice leaf blight, rice blast, rice tungro spherical virus disease, rice brown spot	Kaggle	400	—	YOLOv5	—	62.00%
9	江鹏等^[45] Jiang et al.^[45]	2019	苹果 Apple	褐斑病、花叶病、铁锈病 Apple brown spot, apple mosaic, apple rust	田间采集 Field collection	1 248	—	SSD	—	79.63%
10	Sardoğan等^[29] Sardoğan et al.^[29]	2020	苹果 Apple	黑斑病 Black spot	田间采集 Field collection	700	—	Faster R-CNN	—	84.50%
11	邸洁等^[46] Di et al.^[46]	2020	苹果 Apple	黑星病、黑腐病、雪松锈病 Apple scab, apple rot, cedar apple rust	PlantVillage	1 404	—	Tiny-YOLO	3.57 ms	99.86%
12	李鑫然等^[34] Li et al.^[34]	2021	苹果 Apple	斑点落叶病、褐斑病、花叶病、灰斑病、锈病 Alternaria leaf spot, brown spot, apple mosaic, gray spot, apple rust	PlantVillage、田间采集 PlantVillage, Field collection	2 029	20 050	Faster R-CNN	0.208 s	82.48%
13	Di等^[39] Di et al.^[39]	2022	苹果 Apple	黑星病、黑腐病、雪松锈病 Apple scab, apple rot, cedar apple rust	PlantVillage	1 404	—	Tiny-YOLO	3.57 ms	99.99%
14	Li等^[47] Li et al.^[47]	2022	苹果 Apple	苹果斑点落叶病,等 Alternaria leaf spot, etc	田间采集 Field collection	1 587	24 757	YOLOv5	29.4 ms	96.04%
15	王远志等^[31] Wang et al.^[31]	2022	苹果 Apple	雪松锈病、灰斑病、黑星病 Cedar apple rust, gray spot, apple scab	PlantVillage	730	3 879	Faster R-CNN	0.201 s	87.21%
16	李就好等^[33] Li et al.^[33]	2020	苦瓜 Bitter melon	白粉病、灰斑病、蔓枯病、斑点病 Powdery mildew, gray leaf spot, gummy stem blight, phyllosticta leaf spot	田间采集、 ImageNet Field collection, ImageNet	1 204	10 627	Faster R-CNN	0.322 s	86.39%
17	刘延鑫等^[48] Liu et al.^[48]	2022	烟草 Tobacco	普通花叶病、黄瓜花叶病毒病、赤星病、烟草野火病、气候性斑点病 Ccommon mosaic disease, cucumber mosaic virus disease, scab disease, tobacco wildfire disease, climatic spot disease	田间采集 Field collection	1 750	4 500	YOLOv3	0.31 s	77.00%
18	刘阗宇等^[49] LIU et al.^[49]	2018	葡萄 Grape	褐斑病、白粉病、灰霉病、霜霉病、黑痘病、炭疽病 Brown spot, powdery mildew, botrytis, downy mildew, black pox, anthracnose	田间采集 Field collection	6 000	—	Faster R-CNN	1.416 s	66.47%
19	樊湘鹏等^[50] Fan et al.^[50]	2021	葡萄 Grape	白粉病、霜霉病、黑霉病、花叶病毒病、褐斑病 Powdery mildew, downy mildew, black mould blight, mosaic virus disease, brown spot	田间采集 Field collection	1 990	19 900	CNN	0.327 s	98.02%
20	Wang等^[37] Wang et al.^[37]	2021	番茄 Tomato	早疫病、晚疫病、番茄黄化卷叶病毒病、褐斑病,等 Early blight, late blight, yellow leaf curl virus, brown spot, etc	田间采集、互联网 PlantVillage, Internet	15 000	—	YOLOv3	20.28 ms	96.41%
21	陶国柱等^[51] Tao et al.^[51]	2021	番茄 Tomato	细菌性斑点病、早疫病、晚疫病、病毒病、叶霉病、白粉病 Bacterial leaf spot, early blight, late blight, virus disease, leaf mould, powdery mildew	PlantVillage等互联网渠道 Internet channels such as PlantVillage	4 529	5 574	YOLOv4	13.3 ms	86.23%
22	Islam等^[52] Islam et al.^[52]	2019	马铃薯 Potato	早疫病、晚疫病 Early blight, late blight	PlantVillage	2 152	—	CNN	—	99.43%
23	赵越等^[28] Zhao et al.^[28]	2022	马铃薯 Potato	早疫病、晚疫病 Early blight, late blight	Kaggle	—	8 109	Faster R-CNN	—	99.50%
24	Zhang等^[32] Zhang et al.^[32]	2021	大豆 Soybean	病毒菌、灰斑病、细菌性斑点病 Virus disease, frogeye leaf spot, bacterial spot	田间采集 Field collection	—	2 230	Faster R-CNN	—	83.34%
25	Wang等^[38] Wang et al.^[38]	2022	花生 Peanut	花生褐斑病、花生锈斑病 Rrown spot, peanut rust	PlantVillage、田间采集 PlantVillage, field collection	36 258(Plant Village) +4 631(田间采集) 36 258 (Plant Village)+4 631 (field collection)	—	YOLOv5	15 ms	93.73%
26	Li等^[53] Li et al.^[53]	2022	黄麻 Jute	茎腐病、炭疽病、黑带病、软腐病、尖枯病、枯萎病、黄麻花叶病、黄麻黄化病 Stem rot, anthracnose, black band, soft rot, tip blight, dieback, jute mosaic, jute chlorosis	田间采集 Field collection	4 418	—	YOLOv5	—	96.63%
27	Sert^[30] Sert^[30]	2021	马铃薯、辣椒 Potato, pepper	辣椒细菌性斑点病、马铃薯早疫病、马铃薯晚疫病 Pepper bacterial spot, potato early blight, potato late blight	PlantVillage 、田间采集 PlantVillage, Field collection	544(田间采集)+6 023 (PlantVillage) 544 (field collection)+ 6 023 (PlantVillage)	2 176(田间采集) 2176 (field collection)	Faster R_CNN	—	98.06%
28	Shill 等^[54] Shill et al.^[54]	2021	苹果、番茄等13种植物 13 plants including apple and tomato	苹果黑腐病等17种 17 diseases such as apple rot	PlantDoc	2 598	—	YOLOv4/YOLOv3	—	55.45%

编号 No.	文献 Reference	物种 Species	病害类型 Disease type	数据集获取方式 Dataset obtaining method	分割网络 Framework of segmentation network	准确度 Accuracy/%
1	Jia等^[59] Jia et al.^[59]	柿 Persimmon	叶斑病、灰霉病 Leaf spot, gray mold	田间采集 Field collection	U-Net	89.18
2	Liang等^[60] Liang et al.^[60]	小麦 Wheat	白粉病 Powdery mildew	—	U-Net	91.40
3	Chen等^[61] Chen et al.^[61]	水稻 Rice	细菌性条斑病 Bacterial streak	田间采集 Field collection	U-Net	95.60
4	Agarwal等^[63] Agarwal et al.^[63]	马铃薯 Potato	晚疫病 Late blight	公开数据集 Public dataset	SegNet	94.74
5	Tassis等^[65] Tassis et al.^[65]	咖啡 Coffee	锈病、褐斑病、叶斑病 Rust, brown spot, leaf spot	田间采集 Field collection	PSPNet	93.54
6	Li等^[67] Li et al.^[67]	黄瓜 Cucumber	霜霉病、白粉病 Downy mildew, powdery mildew	田间采集 Field collection	DeepLab V3+	81.23
7	Yao等^[68] Yao et al.^[68]	猕猴桃 Kiwi fruit	褐斑病、溃疡病 Brown spot, canker	田间采集 Field collection	DeepLab V3+	96.60
8	Wang等^[69] Wang et al.^[69]	黄瓜 Cucumber	霜霉病、白粉病、病毒病 Downy mildew, powdery mildew, viral disease	田间采集 Field collection	DeepLab V3+,U-Net	93.27
9	Deng等^[72] Deng et al.^[72]	小麦 Wheat	条锈病 Stripe rust	田间采集 Field collection	SegFormer	72.60

编号 No.	文献 Reference	物种 Species	病害类型 Disease type	数据集获取方式 Dataset obtaining method	分割网络 Framework of segmentation network	准确度 Accuracy/%
1	Jia等^[59] Jia et al.^[59]	柿 Persimmon	叶斑病、灰霉病 Leaf spot, gray mold	田间采集 Field collection	U-Net	89.18
2	Liang等^[60] Liang et al.^[60]	小麦 Wheat	白粉病 Powdery mildew	—	U-Net	91.40
3	Chen等^[61] Chen et al.^[61]	水稻 Rice	细菌性条斑病 Bacterial streak	田间采集 Field collection	U-Net	95.60
4	Agarwal等^[63] Agarwal et al.^[63]	马铃薯 Potato	晚疫病 Late blight	公开数据集 Public dataset	SegNet	94.74
5	Tassis等^[65] Tassis et al.^[65]	咖啡 Coffee	锈病、褐斑病、叶斑病 Rust, brown spot, leaf spot	田间采集 Field collection	PSPNet	93.54
6	Li等^[67] Li et al.^[67]	黄瓜 Cucumber	霜霉病、白粉病 Downy mildew, powdery mildew	田间采集 Field collection	DeepLab V3+	81.23
7	Yao等^[68] Yao et al.^[68]	猕猴桃 Kiwi fruit	褐斑病、溃疡病 Brown spot, canker	田间采集 Field collection	DeepLab V3+	96.60
8	Wang等^[69] Wang et al.^[69]	黄瓜 Cucumber	霜霉病、白粉病、病毒病 Downy mildew, powdery mildew, viral disease	田间采集 Field collection	DeepLab V3+,U-Net	93.27
9	Deng等^[72] Deng et al.^[72]	小麦 Wheat	条锈病 Stripe rust	田间采集 Field collection	SegFormer	72.60

编号 No.	文献 Reference	物种 Species	病害类型 Disease type	数据集获取方式 Dataset obtaining method	准确度 Accuracy/%
1	Zhang等^[75] Zhang et al.^[75]	水稻 Rice	白叶枯病 Leaf blight	田间采集 Field collection	81.00
2	Das等^[74] Das et al.^[74]	水稻 Rice	—	公开数据集 Public dataset	93.28
3	Afzaal等^[76] Afzaal et al.^[76]	草莓 Strawberry	角斑病、花腐病,等 Angular leafspot, blossom bligh, etc.	田间采集 Field collection	82.43
4	Stewart等^[77] Stewart et al.^[77]	玉米 Maize	叶枯病 Leaf blight	田间采集 Field collection	96.00
5	Storey等^[78] Storey et al.^[78]	苹果 Apple	锈病 Apple rust	公开数据集 Public dataset	48.90
6	Mu等^[79] Mu et al.^[79]	梨 Pear	褐斑病、灰斑病、轮纹病 Brown spot, gray spot, ring spot	田间采集 Field collection	88.55

深度学习在作物及其近缘种抗病性智能鉴定上的研究进展

Research progress of deep learning in intelligent identification of disease resistance of crops and their related species

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编号 No.	文献 Reference	物种 Species	病害 Disease	病害等级 Disease severity	数据集获取方式 Dataset obtaining method	数据集图像数量 Image quaity in datase	准确度 Accuracy/ %
1	Wu等^[86] Wu et al.^[86]	番茄 Tomato	早疫病、晚疫病 Early blight, late blight	健康、轻度早疫病、重度早疫病、轻度晚疫病、重度晚疫病 Healthy, mild early blight, severe early blight, mild late blight, severe late blight	公开数据集 Public dataset	2 856	96.40
2	Wang等^[69] Wang et al.^[69]	黄瓜 Cucumber	霜霉病、白粉病、病毒病 Downy mildew, powdery mildew, viral disease	按病斑在叶片中的占比分为L1~L5五个等级 Divided into five levels (L1~L5) based on the percentage of leaf area affected by the lesions	田间采集 Field collection	1 000	92.85
3	Li等^[67] Li et al.^[67]	黄瓜 Cucumber	霜霉病、白粉病 Downy mildew, powdery mildew	健康、病叶 Healthy, diseased leaves	田间采集 Field collection	3 036	95.78
4	Deng等^[72] Deng et al.^[72]	小麦 Wheat	条锈病 Stripe rust	健康、条锈病(宏观疾病指数MDI) Healthy, stripe rust (macro disease index, MDI)	田间采集 Field collection	25 530	99.20
5	Chen等^[61] Chen et al.^[61]	水稻 Rice	细菌性条斑病 Bacterial streak	按病斑在叶片中的占比分为1~5级 Divided into five levels (1-5) based on the percentage of leaf area affected by the lesions	田间采集 Field collection	1 199	94.00
6	何东健等^[87] He et al.^[87]	葡萄 Grape	霜霉病 Downy mildew	健康、前期、中期、后期 Healthy, early stage, mid-stage, late stage	田间采集 Field collection	4 470	99.92
7	刘斌等^[89] Liu et al.^[89]	苹果 Apple	黑星病、锈病 Black spot, apple rust	健康、早期黑星病、晚期黑星病、早期锈病、晚期锈病 Healthy, early stage of black spot, late stage of black spot, early stage of apple rust, late stage of apple rust disease	公开数据集,田间采集 Public dataset, Field collection	20 110	90.82
8	万军杰等^[88] Wan et al.^[88]	苹果等6类植物 6 kinds of plants including apple	黑星病等25类病虫害 25 types of pests and diseases such as apple scab	健康、Ⅰ级、Ⅱ级 Healthy, Level Ⅰ, Level Ⅱ	公开数据集、田间采集 Public dataset, Field collection	13 153	99.35
9	Esgario等^[90] Esgario et al.^[90]	咖啡 Coffee	锈病、褐斑病 Rust, brown spot	健康、非常低、低、高、非常高 Healthy, very low, low, high, and very high	田间采集 Field collection	4 407	86.51

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