Journal of Agricultural Science and Technology ›› 2022, Vol. 24 ›› Issue (10): 79-89.DOI: 10.13304/j.nykjdb.2021.0688
• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles Next Articles
Hao HUANG1(), Shengqiao XIE1, Du CHEN2(), Heng WANG3
Received:
2021-08-12
Accepted:
2021-11-22
Online:
2022-10-15
Published:
2022-10-25
Contact:
Du CHEN
通讯作者:
陈度
作者简介:
黄昊E-mail:cauhuanghao@163.com;
基金资助:
CLC Number:
Hao HUANG, Shengqiao XIE, Du CHEN, Heng WANG. Application and Research Advances on Deep Learning in Apple’s Industry Chain[J]. Journal of Agricultural Science and Technology, 2022, 24(10): 79-89.
黄昊, 谢圣桥, 陈度, 王恒. 深度学习在苹果产业链中的应用与研究进展[J]. 中国农业科技导报, 2022, 24(10): 79-89.
项目 Item | 等级 Grade | ||
---|---|---|---|
优等品 Superior | 一等品 First | 二等品 Second | |
果形 Fruit shape | 具有本品种 应有的特征 Feature match | 允许果形 有轻微缺点 Minor defects | 果形有缺点,但仍保持本品基本特征,不得有畸形果 Fruit shape defective but still maintain the basic characteristics of the product without malformed fruit |
大型果(最大横切面直径) Large(maximum cross section diameter) | ≥70 mm | ≥65 mm | |
中小型果(最大横切面直径) Small and medium(maximum cross section diameter) | ≥60 mm | ≥55 mm | |
果梗 Fruit stem | 果梗完整(不包括商品化处理造成的果梗缺省) Complete (excluding the default of fruit stem caused by commercialization) | 允许果梗轻微损伤 Minor damage | |
富士系 Fuji | 红或条红 90%以上 Red area >90% | 红或条红 80%以上 Red area >80% | 红或条红55%以上 Red area >55% |
嘎拉系 Gala | 红80%以上 Red area >80% | 红70%以上 Red area >70% | 红50%以上 Red area >50% |
Table 1 Fresh apple quality grade requirements[37]
项目 Item | 等级 Grade | ||
---|---|---|---|
优等品 Superior | 一等品 First | 二等品 Second | |
果形 Fruit shape | 具有本品种 应有的特征 Feature match | 允许果形 有轻微缺点 Minor defects | 果形有缺点,但仍保持本品基本特征,不得有畸形果 Fruit shape defective but still maintain the basic characteristics of the product without malformed fruit |
大型果(最大横切面直径) Large(maximum cross section diameter) | ≥70 mm | ≥65 mm | |
中小型果(最大横切面直径) Small and medium(maximum cross section diameter) | ≥60 mm | ≥55 mm | |
果梗 Fruit stem | 果梗完整(不包括商品化处理造成的果梗缺省) Complete (excluding the default of fruit stem caused by commercialization) | 允许果梗轻微损伤 Minor damage | |
富士系 Fuji | 红或条红 90%以上 Red area >90% | 红或条红 80%以上 Red area >80% | 红或条红55%以上 Red area >55% |
嘎拉系 Gala | 红80%以上 Red area >80% | 红70%以上 Red area >70% | 红50%以上 Red area >50% |
品种 Variety | 指标/Index | |
---|---|---|
果实硬度 Hardness/(N·cm-2) | 可溶性固形物 Soluble solid content/% | |
富士系 Fuji | ≥7 | ≥13 |
嘎拉系 Gala | ≥6.5 | ≥12 |
藤木1号 Fujiki | ≥5.5 | ≥11 |
元帅系 Marshal | ≥6.8 | ≥11.5 |
华夏 Huaxia | ≥6.0 | ≥11.5 |
粉红女士 Pink lady | ≥7.5 | ≥13 |
澳洲青苹 Granny Smith | ≥7.0 | ≥12 |
乔纳金 Jonagold | ≥6.5 | ≥13 |
秦冠 Qinguan | ≥7.0 | ≥13 |
国光 Guoguang | ≥7.0 | ≥13 |
华冠 Huaguan | ≥6.5 | ≥13 |
红将军 Red general | ≥6.5 | ≥13 |
Table 2 Reference values for physical and chemical indicators of the main apple varieties[37]
品种 Variety | 指标/Index | |
---|---|---|
果实硬度 Hardness/(N·cm-2) | 可溶性固形物 Soluble solid content/% | |
富士系 Fuji | ≥7 | ≥13 |
嘎拉系 Gala | ≥6.5 | ≥12 |
藤木1号 Fujiki | ≥5.5 | ≥11 |
元帅系 Marshal | ≥6.8 | ≥11.5 |
华夏 Huaxia | ≥6.0 | ≥11.5 |
粉红女士 Pink lady | ≥7.5 | ≥13 |
澳洲青苹 Granny Smith | ≥7.0 | ≥12 |
乔纳金 Jonagold | ≥6.5 | ≥13 |
秦冠 Qinguan | ≥7.0 | ≥13 |
国光 Guoguang | ≥7.0 | ≥13 |
华冠 Huaguan | ≥6.5 | ≥13 |
红将军 Red general | ≥6.5 | ≥13 |
1 | HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science,2006,313:504-507. |
2 | Schmidhuber J. Deep learning in neural networks: an overview [J]. Neural Networks, 2015,61:85-117. |
3 | LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. Nature, 2015, 521(7553): 436-444. |
4 | DARGAN S, KUMAR M, AYYAGARI M R, et al.. A survey of deep learning and its applications: a new paradigm to machine learning [J]. Arch. Comput. Methods Eng., 2020, 27(4): 1071-1092. |
5 | SHRESTHA A, MAHMOOD A. Review of deep learning algorithms and architectures [J]. IEEE Access, 2019,7:53040-53065. |
6 | ALOM M Z, TAHA T M, YAKOPCIC C, et al.. A state-of-the-art survey on deep learning theory and architectures [J]. Electronics, 2019, 8(3):292-357. |
7 | 周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251. |
ZHOU F Y, JIN L P, DONG J. Review of convolution neural network [J]. Chin. J. Computers, 2017,40(6):1229-1251. | |
8 | 邸洁,曲建华.基于Tiny-YOLO的苹果叶部病害检测[J]. 山东师范大学学报(自然科学版), 2020,35(1): 78-83. |
DI J, QU J H. A detection method for apple leaf diseases based on Tiny-YOLO [J]. J. Shandong Norm. Univ. (Nat. Sci.), 2020,35(1): 78-83. | |
9 | FRANCIS M, DEISY C. Disease detection and classification in agricultural plants using convolutional neural networks—avisual understanding [C] // Proceedings of 6th International Conference on Signal Processing and Integrated Networks (SPIN). Nndia Amity, 2019: 1063-1068. |
10 | ZHENG Z, PAN S, ZHANG Y. Fruit tree disease recognition based on convolutional neural networks [C] // Proceedings of International Conferences on Ubiquitous Computing & Communications; Data Science and Computational Intelligence; Smart Computing, Networking and Services. China Shenyang, 2019:118-122. |
11 | AGARWAL M, KALIYAR R K, SINGAL G, et al.. FCNN-LDA: A faster convolution neural network model for Leaf disease identification on apple’s leaf dataset [C] // Proceedings of 12th International Conference on Information & Communication Technology and System (ICTS). Indonesia Surabaya,2019:246-251. |
12 | SON C H. Leaf spot attention network for apple leaf disease identification [C] // Proceedigns of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. USA Seattle, IEEE,2020:229-237. |
13 | NAGARAJU Y, VENKATESH, SWETHA S, et al.. Apple and grape leaf diseases classification using transfer learning via fine-tuned classifier [C] // Proceedigns of 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). India Hyderabad,IEEE,2020:1-6. |
14 | 鲍文霞,吴刚,胡根生,等.基于改进卷积神经网络的苹果叶部病害识别 [J]. 安徽大学学报(自然科学版), 2021,45(1):53-59. |
BAO WENXIA, WU GANG, HU GENSHENG, et al.. Apple leaf disease recognition based on improved convolutional neural network [J]. J. Anhui Univ. (Nat. Sci.), 2021,45(1):53-59. | |
15 | WANG X A, TANG J, WHITTY M. Side-view apple flower mapping using edge-based fully convolutional networks for variable rate chemical thinning [J/OL]. Computers Electron. Agric.,2020,178: 105673 [2021-07-01]. . |
16 | BHATTARAI U, BHUSAL S, MAJEEDY, et al.. Automatic blossom detection in apple trees using deep learning [J]. IFAC-Papers,2020,53(2):15810-15815. |
17 | MAZZIA V, KHALIQ A, SALVETTI F, et al.. Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application [J]. IEEE Access,2020,8:9102-9114. |
18 | WANG D, LI C, SONG H, et al.. Deep learning approach for apple edge detection to remotely monitor apple growth in orchards [J]. IEEE Access,2020,8: 26911-26925. |
19 | CHEN Z, TING D, NEWBURY R, et al. Semantic segmentation for partially occluded apple trees based on deep learning [J]. Computers Electron. Agric.,2021,181:105952-105958. |
20 | LIU C, HAN J, CHEN B, et al.. A novel identification method for apple (Malus domestica Borkh.) cultivars based on a deep convolutional neural network with leaf image input [J]. Symmetry, 2020,12(2):217-235. |
21 | LI J, XIE S, CHEN Z, et al.. A shallow convolutional neural network for apple classification [J]. IEEE Access,2020,8:111683-111692. |
22 | 张力超,马蓉,张垚鑫.改进的LeNet-5模型在苹果图像识别中的应用 [J] .计算机工程与设计, 2018, 39(11):3570-3575. |
ZHANG L C, MA R, ZHANG Y X. Application of improved LeNet-5 model in apple image recognition [J]. Computer Eng. Design, 2018, 39(11):3570-3575. | |
23 | 王丹丹,何东健.基于R-FCN深度卷积神经网络的机器人疏果前苹果目标的识别 [J].农业工程学报,2019,35(3):156-163. |
WANG D D, HE D J. Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network [J]. Trans. Chin. Soc. Agric. Eng.,2019,35(3):156-163. | |
24 | 冯娟,曾立华,刘刚,等.融合多源图像信息的果实识别方法 [J] .农业机械学报,2014,45(2):73-80. |
FENG J, ZENG L H, LIU G, et al.. Fruit Recognition algorithm based on multi-source Images Fusion [J]. Trans. Chin. Soc.Agric. Mach.,2014,45(2):73-80. | |
25 | 宋怀波,何东健,潘景朋.基于凸壳理论的遮挡苹果目标识别与定位方法 [J].农业工程学报,2012, 28(22):174-180. |
SONG H B, HE DO J, PAN J P. Recognition and localization methods of occluded apples based on convex hull theory [J]. Trans. Chin. Soc. Agric. Eng.,2012, 28(22):174-180. | |
26 | 刘晓洋,赵德安,陈玉,等.夜间低照度条件下苹果采摘机器人的图像识别 [J].华中科技大学学报(自然科学版),2015,43(S1):525-528. |
LIU X Y, ZHAO D A, CHEN Y, et al.. Image recognition of apple harvesting robot in artificial light source of low illumination at night [J]. J. Huazhong Univ. Sci. Technol. (Nat. Sci.),2015,43(S1):525-528. | |
27 | 孙飒爽.自然环境下绿色苹果目标的识别与分割方法研究 [D].杨凌:西北农林科技大学,2019. |
SUN S S. Recognition and segmentation of green apple targets in natural environments [D]. Yangling:Northwest A&F University,2019. | |
28 | 张亚静,李民赞,乔军,等.一种基于图像特征和神经网络的苹果图像分割算法 [J] .光学学报,2008, 28(11):2104-2108. |
ZHANG Y J, LI M Z, QIAO J, et al.. Segmentation algorithm for apple recognition using image features and artificial neural network [J]. Acta Optica Sin.,2008, 28(11):2104-2108. | |
29 | 王津京,赵德安,姬伟,等.基于BP神经网络的苹果图像分割算法 [J] .农机化研究,2008(11):19-21. |
WANG J J, ZHAO D A, JI W, et al.. Segmentation of apple image by BP neural network using in apple harvesting robot [J]. J. Agric. Mechanization Res., 2008(11):19-21 | |
30 | GENÉ-MOLA J, VILAPLANA V, ROSELL-POLO J R, et al.. Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities [J]. Computers Electron. Agric.,2019,162:689-698. |
31 | LFABC D, YM D, XIN Z D, et al.. Faster R-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting [J]. Biosys. Eng., 2020, 197:245-256. |
32 | CHU P, LI Z, LAMMERS K, et al.. Deep learning-based apple detection using a suppression mask R-CNN [J]. Pattern Recognition Lett.,2021,147:206-211. |
33 | 周晓彦,王珂,李凌燕.基于深度学习的目标检测算法综述 [J].电子测量技术, 2017,40(11):89-93. |
ZHOU X Y, WANG K, LI L Y. Review of object detection based on deep learning [J]. Electron. Measure. Technol., 2017,40(11):89-93. | |
34 | KUZNETSOVA A, MALEVA T, SOLOVIEV V. Using YOLOv3 algorithm with pre- and post-processing for apple detection in fruit-harvesting robot [J]. Agronomy, 2020,10(7):1016-1034. |
35 | 武星,齐泽宇,王龙军,等.基于轻量化YOLOv3卷积神经网络的苹果检测方法[J].农业机械学报,2020,51(8):17-25. |
WU X, QI Z Y, WANG L J, et al.. Apple detection method based on light-YOLOv3 convolutional neural network [J]. Trans. Chin. Soc. Agric. Mach., 2020,51(8):17-25. | |
36 | 彭红星,黄博,邵园园,等.自然环境下多类水果采摘目标识别的通用改进SSD模型[J].农业工程学报,2018,34(16):155-162. |
PENG H X, HUANG B, SHAO Y Y, et al.. General improved SSD model for picking object recognition of multiple fruits in natural environment [J]. Trans. Chin. Soc. Agric. Eng., 2018,34(16):155-162. | |
37 | 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. 鲜苹果: [S].北京:中国标准出版社,2008. |
38 | 何进荣,石延新,刘斌,等.基于深度学习的富士苹果外部品质分级方法 [J]. 农业机械学报,2021, 4(25):1-12. |
HE J R, SHI Y X, LIU B, et al.. Extrenal quality granding method of Fuji apple based on deep learning [J]. Trans. Chin. Soc. Agric. Mach.,2021, 4(25):1-12. | |
39 | 薛勇,王立扬,张瑜,等.基于GoogLeNet深度迁移学习的苹果缺陷检测方法[J].农业机械学报,2020, 51(7):30-35. |
XUE Y, WANG L Y, ZHANG Y, et al.. Defect detection method of apples based on GoogLeNet deep transfer learning [J]. Trans. Chin. Soc. Agric. Mach.,2020, 51(7):30-35. | |
40 | FAN S, LI J, ZHANG Y, et al.. Online detection of defective apples using computer vision system combined with deep learning methods [J]. J. Food Eng.,2020,286:110102-1101011. |
41 | OHALI Y A. Computer vision based datad fruit grading system: Design and implementation [J]. J. King Saud Univ. Computer Inform. Sci.,2011,23(1):29-36. |
42 | SOFU M M, ER O, KAYACAN M C, et al.. Design of an automatic apple sorting system using machine vision [J]. Computers Electron. Agric.,2016, 127:395-405. |
43 | 王立扬,张瑜,沈群,等.基于改进型LeNet-5的苹果自动分级方法 [J]. 中国农机化学报,2020,41(7): 105-110. |
WANG LIYANG, ZHANG YU, SHEN QUN, et al.. Automatic detection and grading method of apples based on improved LeNet-5 [J]. J. Chin. Agric. Mechan., 2020,41(7):105-110. | |
44 | 石瑞瑶. 基于机器视觉的苹果在线分级系统平台的研究 [D]. 沈阳:沈阳农业大学,2018. |
SHI RUIYAO. Research on apple online grading system platform based on machine vision [D]. Shenyang: Shenyang Agricultural University,2018. | |
45 | 凌强. 基于机器视觉的苹果品质分级技术的研发[D].哈尔滨:黑龙江大学,2019. |
LING QIANG. Research and development of apple quality grading technology based on machine vision [D]. Harbin: Heilongjiang University,2019 | |
46 | 朱启兵,黄敏,赵桂林.基于邻域粗糙集和高光谱散射图像的苹果粉质化检测[J].农业机械学报, 2011,42(10):154-157. |
ZHU Q B, HUANG M, ZHAO G L. Apple mealiness detection based on neighborhood rough set and hyperspectral scattering image [J]. Trans. Chin. Soc. Agric. Mach., 2011,42(10):154-157. | |
47 | 曹玉栋,祁伟彦,李娴,等.苹果无损检测和品质分级技术研究进展及展望[J].智慧农业,2019, 1(3):29-45. |
CAO Y D, QI W Y, LI X, et al.. Research progress and prospect on non-destructive detection and quality grading technology of apple [J]. Smart Agric.,2019, 1(3):29-45. | |
48 | LASHGARI M, IMANMEHR A, TAVAKOLI H. Fusion of acoustic sensing and deep learning techniques for apple mealiness detection [J]. J. Food Sci. Technol.,2020,57(6):2233-2240. |
49 | BAI Y, XIONG Y, HUANG J, et al.. Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features [J]. Postharvest Biol. Technol.,2019,156:110943-110952. |
50 | 徐焕良,孙云晓,曹雪莲,等.基于光子传输模拟的苹果品质检测研究[J].农业机械学报,2021,52(8): 1-13. |
XU H L, SUN Y X, CAO X L, et al.. Research on apple quality detection based on photon transmission simulation [J]. Trans. Chin. Soc. Agric. Mach., 2021,52(8): 1-13. | |
51 | MARCUS G, DAVIS E. Rebooting AI: Building Artificial Intelligence We Can Trust [M] .Vintage,2019. |
52 | 马岽奡,唐娉,赵理君,等.深度学习图像数据增广方法研究综述[J].中国图象图形学报,2021, 26(3): 487-502. |
MA D G, TANG P, ZHAO L J, et al.. Review of data augmentation for image in deep learning [J]. J. Image Graphics, 2021, 26(3): 487-502. | |
53 | PARK W, KIM D, LU Y, et al.. Relational knowledge distillation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA California, IEEE, 2019: 3967-3976. |
54 | YOUNG S, WANG Z, TAUBMAN D, et al.. Transform quantization for CNN compression [J]. Proceedigns of IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2021:5700-5714. |
[1] | Yue ZHAO, Yong WEI, Huiyong SHAN, Zhimin MU, ZHANGJianxin, Haiyun WU, Hui ZHAO, Jianlong HU. Wheat Ear Detection Method Based on Deep Learning [J]. Journal of Agricultural Science and Technology, 2022, 24(9): 96-105. |
[2] | Haitao LIU, Xin HAN, Yubin LAN, Lili YI, Baoju WANG, Lihua CUI. Precise Recognition Method of Cotton Top Buds Based on YOLOv4 Network [J]. Journal of Agricultural Science and Technology, 2022, 24(8): 99-108. |
[3] | Kui FANG, Cheng LI, Xiao HE, Yineng CHEN. Research on Multi-angle Identification of Grape Leaf Disease Based on 3D Reconstruction [J]. Journal of Agricultural Science and Technology, 2022, 24(7): 86-96. |
[4] | Jianwei WU, Jie HUANG, Xiaofei XIONG, Han GAO, Xiangyang QIN. Research and Application of Intelligent Recognition Method of Peach Tree Diseases Based on AI [J]. Journal of Agricultural Science and Technology, 2022, 24(5): 111-118. |
[5] | Yinyan GAO, Yi SUN, Baochun LI. Estimating of Wheat Ears Number in Field Based on RGB Images Using Unmanned Aerial Vehicle [J]. Journal of Agricultural Science and Technology, 2022, 24(3): 103-110. |
[6] | ZHOU Huiru, WU Boming. Advances in Research on Deep Learning for Crop Disease Image Recognition [J]. Journal of Agricultural Science and Technology, 2021, 23(5): 61-68. |
[7] | LYU Chunyang, LIU Shengping, GUO Xiuming, XIAO Shunfu, LIU Dazhong, YANG Feifei, LI Luhua. Detection of Honeybee Based on SSD Model [J]. Journal of Agricultural Science and Technology, 2021, 23(5): 98-107. |
[8] | CHEN Jun, ZHANG Qi, YANG Mengyu, YUAN Zhenyang. Impacts of Orchard Herbage-mulching on Photosynthetic Characteristics and Leaf Quality of Apples in Arid Desert Area [J]. Journal of Agricultural Science and Technology, 2021, 23(5): 160-167. |
[9] | YAO Yingfang, LIU Feng, ZHANG Haidong, LI Chao, ZHOU Haijun, WANG Man. Research on Octagon Color and Fruit Shape Recognition Based on Machine Vision [J]. Journal of Agricultural Science and Technology, 2021, 23(11): 110-120. |
[10] | LI Tai, LU Shijun, HUANG Jiazhang, CHEN Lei, FAN Xieyu. Research Progress of Apple Quality Evaluation Standards [J]. Journal of Agricultural Science and Technology, 2021, 23(11): 121-130. |
[11] | YANG Zhen1,2, YANG Xin1*, YANG Xiaobin1, WANG Pengfei1, LI Jianping1, LIU Hongjie1, LI Xuejun1. Parameter Optimization Design of Apple Seedling Stubble Cutter Based on Virtual Orthogonal Test [J]. Journal of Agricultural Science and Technology, 2021, 23(1): 98-106. |
[12] | XU Beibei1, WANG Wensheng1,2*, GUO Leifeng1, CHEN Guipeng3. A Review and Future Prospects on Cattle Recognition Based on Non-contact Identification [J]. Journal of Agricultural Science and Technology, 2020, 22(7): 79-89. |
[13] |
WANG Mengmeng1, DANG Hongzhong2*, LI Gangtie1, FENG Jinchao2, YAN Jingqiuzi1, HU Yang1, LI Xing1, YANG Chao1.
Sap Flux Density Characteristics of Apple Orchards and Their Relationship with Environmental Factors in Gully Region of Loess Plateau
[J]. Journal of Agricultural Science and Technology, 2020, 22(7): 140-147.
|
[14] | ZHANG Jie1,2, MA Yajun1, HE Zhibin1, GAO Fangfang1, ZHANG Shaohua1, WANG Chaoran1, ZHAO Danchen1. Application of Microbial Fertilizer Instead of Fertilizer in Apple Planting [J]. Journal of Agricultural Science and Technology, 2019, 21(7): 128-135. |
[15] | DONG Wei1, QIAN Rong1, ZHANG Jie 2*, ZHANG Liping1, CHEN Hongbo2, ZHANG Meng1, ZHU Jingbo1, BU Yingqiao3. Vegetable Lepidopteran Pest Auto Recognition and Detection Counting Based on Deep Learning [J]. Journal of Agricultural Science and Technology, 2019, 21(12): 76-84. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||