|本期目录/Table of Contents|

[1]张友为,王鑫鑫,范晓飞.基于深度学习的玉米和番茄病虫害检测技术研究进展[J].江苏农业科学,2024,52(10):10-20.
　Zhang Youwei,et al.Research progress on corn and tomato diseases and pests detection technology based on deep learning[J].Jiangsu Agricultural Sciences,2024,52(10):10-20.
点击复制

基于深度学习的玉米和番茄病虫害检测技术研究进展(PDF)
分享到：

《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第52卷
期数:
2024年第10期
页码:
10-20
栏目:
专论与综述
出版日期:
2024-05-20

文章信息/Info

Title:
Research progress on corn and tomato diseases and pests detection technology based on deep learning
作者:
张友为1; 王鑫鑫2; 3; 范晓飞1
1.河北农业大学机电工程学院，河北保定 071000； 2.河北农业大学河北省山区研究所，河北保定 071000；3.国家北方山区农业工程技术研究中心，河北保定 071001
Author(s):
Zhang Youwei; et al
关键词:
深度学习; 玉米; 番茄; 病虫害检测; 传感器技术; 遥感技术
Keywords:
-
分类号:
S126；S127
DOI:
-
文献标志码:
A
摘要:
近年来，病虫害严重影响了农作物的生长和产量，在当前人口剧增、粮食短缺的背景下，解决这一问题具有急迫性和重要性。因此，深度学习凭借学习能力强和高准确性等优势，逐渐成为农业病虫害检测技术的研究热点之一。深度学习结合多种技术可以更加高效地帮助农民检测病虫害，从而及时采取措施对农作物病虫害进行防治，提高农作物产量和质量。本文以玉米和番茄为研究对象，针对农作物病虫害检测技术对病虫害检测研究中常用的深度学习模型进行了概述，并分别对深度学习与传感器技术和遥感技术结合的病虫害检测系统和不同应用场景上深度学习结合不同技术对病虫害检测起到的应用效果进行阐述；同时总结了玉米和番茄的常见害虫种类、害虫体型特点和啃食特点。最后，讨论了深度学习技术在实际应用中存在的问题和未来深度学习技术的发展方向。深度学习与先进技术的结合将为农民和农作物专家提供有效的工具，帮助他们及时发现和应对病虫害问题，提高农作物的产量和质量。
Abstract:
-

参考文献/References:

［1］张帆，郭思媛，任方涛，等. 基于改进YOLO v3的玉米叶片气孔自动识别与测量方法［J］. 农业机械学报，2023，54(2)：216-222.
［2］Hesamian M H，Jia W J，He X J，et al. Deep learning techniques for medical image segmentation：achievements and challenges［J］. Journal of Digital Imaging，2019，32(4)：582-596.
［3］Wang J，Liu C，Fu T，et al. Research on automatic target detection and recognition based on deep learning［J］. Journal of Visual Communication and Image Representation，2019，60：44-50.
［4］Patrício D I，Rieder R. Computer vision and artificial intelligence in precision agriculture for grain crops：a systematic review［J］. Computers and Electronics in Agriculture，2018，153：69-81.
［5］Mohammad-Razdari A，Rousseau D，Bakhshipour A，et al. Recent advances in e-monitoring of plant diseases［J］. Biosensors and Bioelectronics，2022，201：113953.
［6］Patel R，Mitra B，Vinchurkar M，et al. Plant pathogenicity and associated/related detection systems.A review［J］. Talanta，2023，251：123808.
［7］Mahlein A K. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping［J］. Plant Disease，2016，100(2)：241-251.
［8］Chiwaki K，Nagamori S，Inoue Y. Predicting bacterial wilt disease of tomato plants using remotely sensed thermal imagery［J］. Journal of Agricultural Meteorology，2005，61(3)：153-164.
［9］Sankaran S，Maja J M，Buchanon S，et al. Huanglongbing (citrus greening) detection using visible，near infrared and thermal imaging techniques［J］. Sensors，2013，13(2)：2117-2130.
［10］Xie C Q，Shao Y N，Li X L，et al. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging［J］. Scientific Reports，2015，5：16564.
［11］Sankaran S，Mishra A，Ehsani R，et al. A review of advanced techniques for detecting plant diseases［J］. Computers and Electronics in Agriculture，2010，72(1)：1-13.
［12］Lowe A，Harrison N，French A P. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress［J］. Plant Methods，2017，13：80.
［13］Kamilaris A，Prenafeta-Boldú F X. A review of the use of convolutional neural networks in agriculture［J］. The Journal of Agricultural Science，2018，156(3)：312-322.
［14］Terentev A，Dolzhenko V，Fedotov A，et al. Current state of hyperspectral remote sensing for early plant disease detection：a review［J］. Sensors，2022，22(3)：757.
［15］Zheng C W，Abd-Elrahman A，Whitaker V. Remote sensing and machine learning in crop phenotyping and management，with an emphasis on applications in strawberry farming［J］. Remote Sensing，2021，13(3)：531.
［16］Velusamy P，Rajendran S，Mahendran R K，et al. Unmanned aerial vehicles (UAV) in precision agriculture：applications and challenges［J］. Energies，2021，15(1)：217.
［17］Kaivosoja J，Hautsalo J，Heikkinen J，et al. Reference measurements in developing UAV systems for detecting pests，weeds，and diseases［J］. Remote Sensing，2021，13(7)：1238.
［18］王鹏新，田惠仁，张悦，等. 基于深度学习的作物长势监测和产量估测研究进展［J］. 农业机械学报，2022，53(2)：1-14.
［19］李静雪. 基于深度学习的农作物产量预测［D］. 北京：华北电力大学，2020.
［20］Guo Y，Zhang J，Yin C X，et al. Plant disease identification based on deep learning algorithm in smart farming［J］. Discrete Dynamics in Nature and Society，2020，2020：1-11.
［21］Kamilaris A，Prenafeta-Boldú F X. Deep learning in agriculture：a survey［J］. Computers and Electronics in Agriculture，2018，147：70-90.
［22］Alruwaili M，Siddiqi M H，Khan A，et al. RTF-RCNN：an architecture for real-time tomato plant leaf diseases detection in video streaming using faster-RCNN［J］. Bioengineering，2022，9(10)：565.
［23］Li D S，Wang R J，Xie C J，et al. A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network［J］. Sensors，2020，20(3)：578.
［24］Raj N，Perumal S，Singla S，et al. Computer aided agriculture development for crop disease detection by segmentation and classification using deep learning architectures［J］. Computers and Electrical Engineering，2022，103：108357.
［25］Saleem M H，Khanchi S，Potgieter J，et al. Image-based plant disease identification by deep learning meta-architectures［J］. Plants，2020，9(11)：1451.
［26］Liu L，Wang R J，Xie C J，et al. PestNet：an end-to-end deep learning approach for large-scale multi-class pest detection and classification［J］. IEEE Access，2019，7：45301-45312.
［27］耿艳利，林彦伯，付艳芳，等. 基于双扩张层和旋转框定位的群猪目标检测算法研究［J］. 农业机械学报，2023，54(4)：323-330.
［28］Tee C A T，Teoh Y X，Yee P L，et al. Discovering the Ganoderma boninense detection methods using machine learning：a review of manual，laboratory，and remote approaches［J］. Ieee Access，2021，9：105776-105787.
［29］Putnam M L. Evaluation of selected methods of plant disease diagnosis［J］. Crop Protection，1995，14(6)：517-525.
［30］Revill P A，Wright P J.RT-PCR detection of dsRNAs associated with La France disease of the cultivated mushroom Agaricus bisporus (Lange) Imbach［J］. Journal of Virological Methods，1997，63(1/2)：17-26.
［31］Shorten C，Khoshgoftaar T. A survey on image data augmentation for deep learning［J］. Journal of Big Data，2019，6：1-48.
［32］陆丽娜. 农业科学数据监管模型构建及应用研究［D］. 长春：吉林大学，2018.
［33］Meena S D，Susank M，Guttula T，et al. Crop yield improvement with weeds，pest and disease detection［J］. Procedia Computer Science，2023，218：2369-2382.
［34］Jha K，Doshi A，Patel P，et al. A comprehensive review on automation in agriculture using artificial intelligence［J］. Artificial Intelligence in Agriculture，2019，2：1-12.
［35］Wang Y H，Su W H.Convolutional neural networks in computer vision for grain crop phenotyping：a review［J］. Agronomy，2022，12(11)：2659.
［36］Mishra S，Sachan R，Rajpal D. Deep convolutional neural network based detection system for real-time corn plant disease recognition［J］. Procedia Computer Science，2020，167：2003-2010.
［37］Waheed A，Goyal M，Gupta D，et al. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf［J］. Computers and Electronics in Agriculture，2020，175：105456.
［38］Zhang X H，Qiao Y，Meng F F，et al. Identification of maize leaf diseases using improved deep convolutional neural networks［J］. IEEE Access，2018，6：30370-30377.
［39］Priyadharshini R A，Arivazhagan S，Arun M，et al. Maize leaf disease classification using deep convolutional neural networks［J］. Neural Computing and Applications，2019，31(12)：8887-8895.
［40］Li Z X，Li C，Deng L F，et al. Improved AlexNet with inception-v4 for plant disease diagnosis［J］. Computational Intelligence and Neuroscience，2022，2022：5862600.
［41］徐会杰，黄仪龙，刘曼. 基于改进YOLO v3模型的玉米叶片病虫害检测与识别研究［J］. 南京农业大学学报，2022，45(6)：1276-1285.
［42］Tortorici S，Biondi A，Pérez-Hedo M，et al. Plant defences for enhanced integrated pest management in tomato［J］. Annals of Applied Biology，2022，180(3)：328-337.
［43］Brahimi M，Boukhalfa K，Moussaoui A. Deep learning for tomato diseases：classification and symptoms visualization［J］. Applied Artificial Intelligence，2017，31(4)：299-315.
［44］Maeda-Gutiérrez V，Galván-Tejada C E，Zanella-Calzada L A，et al. Comparison of convolutional neural network architectures for classification of tomato plant diseases［J］. Applied Sciences，2020，10(4)：1245.
［45］贾少鹏，高红菊，杭潇. 基于深度学习的农作物病虫害图像识别技术研究进展［J］. 农业机械学报，2019，50(增刊1)：313-317.
［46］Prabhakar M，Purushothaman R，Awasthi D P.Deep learning based assessment of disease severity for early blight in tomato crop［J］. Multimedia Tools and Applications，2020，79(39/40)：28773-28784.
［47］Kibriya H，Rafique R，Ahmad W，et al. Tomato leaf disease detection using convolution neural network［C］//2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). Islamabad，Pakistan.IEEE，2021：346-351.
［48］刘志勇，张丽秀，钟婷婷，等. 基于改进leNet-5的番茄病虫害识别的研究［J］. 赣南师范大学学报，2020，41(6)：70-74.
［49］Wspanialy P，Moussa M. A detection and severity estimation system for generic diseases of tomato greenhouse plants［J］. Computers and Electronics in Agriculture，2020，178：105701.
［50］Wang X W，Liu J，Liu G X. Diseases detection of occlusion and overlapping tomato leaves based on deep learning［J］. Frontiers in Plant Science，2021，12：792244.
［51］Liu G X，Nouaze J C，Touko Mbouembe P L，et al. YOLO-tomato：a robust algorithm for tomato detection based on YOLO v3［J］. Sensors，2020，20(7)：2145.
［52］Hu R J，Zhang S，Wang P，et al. The identification of corn leaf diseases based on transfer learning and data augmentation［C］//Proceedings of the 2020 3rd International Conference on Computer Science and Software Engineering.Beijing China.New York，NY，USA：ACM，2020：58-65.
［53］Sun X L，Wei J S. Identification of maize disease based on transfer learning［J］. Journal of Physics：Conference Series，2020，1437(1)：012080.
［54］胡文艺，王洪坤，杜育佳.基于SE模块和ResNet的番茄病虫害识别方法［J］. 农业工程，2022，12(9)：33-40.
［55］Liu J，Wang X W. Early recognition of tomato gray leaf spot disease based on MobileNet v2-YOLO v3 model［J］. Plant Methods，2020，16：83.
［56］郭辉，陈海洋，高国民，等. 基于YOLO v5m的红花花冠目标检测与空间定位方法［J］. 农业机械学报，2023，54(7)：272-281.
［57］唐詹，柏召，刁磊，等. 基于注意力池化和堆叠式结构的病虫害文献识别模型［J］. 农业机械学报，2021，52(增刊1)：178-184.
［58］王美华，吴振鑫，周祖光. 基于注意力改进CBAM的农作物病虫害细粒度识别研究［J］. 农业机械学报，2021，52(4)：239-247.
［59］Liu J，Wang X W，Miao W Q，et al. Tomato pest recognition algorithm based on improved YOLO v4［J］. Frontiers in Plant Science，2022，13：814681.
［60］Xu Y L，Kong S L，Gao Z M，et al. HLNet model and application in crop leaf diseases identification［J］. Sustainability，2022，14(14)：8915.
［61］Rahaman M M，Azharuddin M. Wireless sensor networks in agriculture through machine learning：a survey［J］. Computers and Electronics in Agriculture，2022，197：106928.
［62］Zou X G，Liu W C，Huo Z Q，et al. Current status and prospects of research on sensor fault diagnosis of agricultural internet of things［J］. Sensors，2023，23(5)：2528.
［63］García-Berná J A，Ouhbi S，Benmouna B，et al. Systematic mapping study on remote sensing in agriculture［J］. Applied Sciences，2020，10(10)：3456.
［64］Sun J，Lai Z L，Di L P，et al. Multilevel deep learning network for county-level corn yield estimation in the U.S.corn belt［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2020，13：5048-5060.
［65］Darwin B，Dharmaraj P，Prince S，et al. Recognition of bloom/yield in crop images using deep learning models for smart agriculture：a review［J］. Agronomy，2021，11(4)：646.
［66］Dhau I，Adam E，Mutanga O，et al. Detecting the severity of maize streak virus infestations in maize crop using in situ hyperspectral data［J］. Transactions of the Royal Society of South Africa，2018，73(1)：8-15.
［67］Vong C N，Conway L S，Feng A J，et al. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning［J］. Computers and Electronics in Agriculture，2022，198：107008.
［68］Vong C N，Conway L S，Zhou J F，et al. Early corn stand count of different cropping systems using UAV-imagery and deep learning［J］. Computers and Electronics in Agriculture，2021，186：106214.
［69］Bouguettaya A，Zarzour H，Kechida A，et al. Deep learning techniques to classify agricultural crops through UAV imagery：a review［J］. Neural Computing & Applications，2022，34(12)：9511-9536.
［70］Ishengoma F S，Rai I A，Said R N. Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks［J］. Computers and Electronics in Agriculture，2021，184：106124.
［71］申华磊，苏歆琪，赵巧丽，等. 基于深度学习的无人机遥感小麦倒伏面积提取方法［J］. 农业机械学报，2022，53(9)：252-260，341.
［72］Ahmad A，Saraswat D，Gamal A，et al. Comparison of deep learning models for corn disease region location，identification of disease type，and severity estimation using images acquired from UAS-mounted and handheld sensors［J］. Journal of the ASABE，2022，65(6)：1433-1442.
［73］Osco L P，Marcato J Jr，Marques Ramos A P，et al. A review on deep learning in UAV remote sensing［J］. International Journal of Applied Earth Observation and Geoinformation，2021，102：102456.
［74］Stewart E L，Wiesner-Hanks T，Kaczmar N，et al. Quantitative phenotyping of northern leaf blight in UAV images using deep learning［J］. Remote Sensing，2019，11(19)：2209.
［75］Wiesner-Hanks T，Wu H，Stewart E，et al. Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data［J］. Frontiers in Plant Science，2019，10：1550.
［76］Wu H，Wiesner‐Hanks T，Stewart E L，et al. Autonomous detection of plant disease symptoms directly from aerial imagery［J］. The Plant Phenome Journal，2019，2(1)：1-9.
［77］Bouguettaya A，Zarzour H，Kechida A，et al. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images［J］. Cluster Computing，2023，26(2)：1297-1317.
［78］Rehman T，Jin J. Deep adversarial domain adaptation for hyperspectral calibration model transfer among plant phenotyping systems［J］. Biosystems Engineering，2022，224：246-258.
［79］Thangaraj R，Anandamurugan S，Pandiyan P，et al. Artificial intelligence in tomato leaf disease detection：a comprehensive review and discussion［J］. Journal of Plant Diseases and Protection，2021，129(3)：1-20.
［80］周占坤. 基于THz-NIR高光谱相融合的番茄病虫害检测研究［D］. 镇江：江苏大学，2022.
［81］Brahim B，Raziyeh P，Sajad S，et al. Comparison of classic classifiers，metaheuristic algorithms and convolutional neural networks in hyperspectral classification of nitrogen treatment in tomato leaves［J］. Remote Sensing，2022，14(24)：6366.
［82］Yang W，Nigon T，Hao Z Y，et al. Estimation of corn yield based on hyperspectral imagery and convolutional neural network［J］. Computers and Electronics in Agriculture，2021，184：106092.
［83］Yang W，Yang C，Hao Z Y，et al. Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network［J］. IEEE Access，2019，7：118239-118248.
［84］Zhang J，Dai L M，Cheng F.Identification of corn seeds with different freezing damage degree based on hyperspectral reflectance imaging and deep learning method［J］. Food Analytical Methods，2021，14(2)：389-400.
［85］Pang L，Men S，Yan L，et al. Rapid vitality estimation and prediction of corn seeds based on spectra and images using deep learning and hyperspectral imaging techniques［J］. IEEE Access，2020，8：123026-123036.
［86］Guerri M F，Distante C，Spagnolo P，et al. Deep learning techniques for hyperspectral image analysis in agriculture：a review［J/OL］. 2023：arXiv：2304.13880.https：//arxiv.org/abs/2304.13880.pdf.
［87］Khan A，Vibhute A D，Mali S，et al. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications［J］. Ecological Informatics，2022，69：101678.
［88］Zhang K P，Yu Z H，Jiang S X，et al. Association of host plant growth and weed occurrence with armyworm (Mythimna separata) damage in corn fields［J］. Journal of Integrative Agriculture，2018，17(7)：1538-1544.
［89］Li L T，Zhu Y B，Ma J F，et al. An analysis of the Athetis lepigone transcriptome from four developmental stages［J］. PLoS One，2013，8(9)：e73911.
［90］Xu Y Y，Wei W，Lin G Z，et al. The Ras/MAPK pathway is required for regenerative growth of wing discs in the black cutworm Agrotis ypsilon［J］. Insect Biochemistry and Molecular Biology，2021，131：103552.
［91］Chen H L，Lin L L，Xie M H，et al. Influence of constant temperature on reproductive parameters of Holotrichia oblita (Coleoptera：Scarabaeidae)［J］. Journal of Insect Science，2015，15(1)：93.
［92］Ishengoma F S，Rai I A，Ngoga S R.Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAV-based images［J］. Ecological Informatics，2022，67：101502.
［93］Lv J W，Li W Y，Fan M Y，et al. Detecting pests from light-trapping images based on improved YOLO v3 model and instance augmentation［J］. Frontiers in Plant Science，2022，13：939498.
［94］Feng J D，Sun Y Q，Zhang K F，et al. Autonomous detection of Spodoptera frugiperda by feeding symptoms directly from UAV RGB imagery［J］. Applied Sciences，2022，12(5)：2592.
［95］Jiu M，Zhou X P，Tong L，et al. Vector-virus mutualism accelerates population increase of an invasive whitefly［J］. PLoS One，2007，2(1)：e182.
［96］Domingues T，Brando T，Ribeiro R，et al. Insect detection in sticky trap images of tomato crops using machine learning［J］. Agriculture，2022，12(11)：1967.
［97］Mohanty S P，Hughes D P，Salathé M.Using deep learning for image-based plant disease detection［J］. Frontiers in Plant Science，2016，7：1419.
［98］Altalak M，Ammad Uddin M，Alajmi A，et al. Smart agriculture applications using deep learning technologies：a survey［J］. Applied Sciences，2022，12(12)：5919.
［99］Khan R U，Khan K，Albattah W，et al. Image-based detection of plant diseases：from classical machine learning to deep learning journey［J］. Wireless Communications and Mobile Computing，2021，2021：1-13.
［100］Manavalan R. Automatic identification of diseases in grains crops through computational approaches：a review［J］. Computers and Electronics in Agriculture，2020，178：105802.
［101］Sladojevic S，Arsenovic M，Anderla A，et al. Deep neural networks based recognition of plant diseases by leaf image classification［J］. Computational Intelligence and Neuroscience，2016，2016：3289801.
［102］Fink O，Wang Q，Svensén M，et al. Potential，challenges and future directions for deep learning in prognostics and health management applications［J］. Engineering Applications of Artificial Intelligence，2020，92：103678.
［103］宋怀波，尚钰莹，何东健. 果实目标深度学习识别技术研究进展［J］. 农业机械学报，2023，54(1)：1-19.
［104］Verma S，Chug A，Singh A P. Prediction models for identification and diagnosis of tomato plant diseases［C］//2018 International Conference on Advances in Computing，Communications and Informatics (ICACCI).Bangalore，India.IEEE，2018：1557-1563.

相似文献/References:

[1]孙建伟.水涝胁迫对玉米细胞保护酶同工酶的影响[J].江苏农业科学,2013,41(04):85.
[2]刘荣,张卫建,齐华,等.密植型玉米“中单909”高产群体结构特征[J].江苏农业科学,2013,41(05):56.
　Liu Rong,et al.Study on high yield population structure of close planting maize cultivar “Zhongdan 909”[J].Jiangsu Agricultural Sciences,2013,41(10):56.
[3]沈浜凯,肖龙云,冯乃杰,等.黄腐酸和AM真菌对玉米幼苗抗旱性的影响[J].江苏农业科学,2013,41(05):64.
　Shen Bangkai,et al.Effects of fulvic acid and AM fungi on drought resistance of maize seedlings[J].Jiangsu Agricultural Sciences,2013,41(10):64.
[4]张金然,缑艳霞,孙丽鹏.固氮螺菌157对玉米、向日葵的促生长作用[J].江苏农业科学,2014,42(12):116.
　Zhang Jinran,et al.Effects of Azospirillum 157 on growth of maize and sunflower[J].Jiangsu Agricultural Sciences,2014,42(10):116.
[5]白小军,吴燕,牛艳,等.玉米中乙草胺和莠去津残留量GC-MS/MS分析法的建立[J].江苏农业科学,2014,42(11):334.
　Bai Xiaojun,et al().Establishment of GC-MS/MS analysis method of acetochlor and atrazine residues in maize[J].Jiangsu Agricultural Sciences,2014,42(10):334.
[6]邹晓威,王娜,刘芬,等.玉米抗病相关基因在玉米与玉米丝黑穗病菌、玉米黑粉病菌互作过程中的表达差异分析[J].江苏农业科学,2014,42(11):150.
　Zou Xiaowei,et al(0).Different expression of resistance-related genes between Sporisorium reilianum and Ustilago maydis interact with corn[J].Jiangsu Agricultural Sciences,2014,42(10):150.
[7]杨洪兴,陈静,陈艳萍.江苏省玉米机械化生产的发展及育种对策思考[J].江苏农业科学,2014,42(11):116.
　Yang Hongxing,et al().Development and breeding strategy of mechanized production of maize in Jiangsu Province[J].Jiangsu Agricultural Sciences,2014,42(10):116.
[8]张丽妍,霍剑锋,孟繁盛,等.不同肥料、施肥水平及施用方法对玉米产量、性状及效益的影响[J].江苏农业科学,2014,42(11):119.
　Zhang Liyan,et al (9).Effects of different fertilizers，fertilizer levels and fertilizing methods on yield，characters and benefit of maize[J].Jiangsu Agricultural Sciences,2014,42(10):119.
[9]王雷,崔震海,张立军.玉米C4型PEPC全长基因的克隆与表达载体构建[J].江苏农业科学,2014,42(11):26.
　Wang Lei,et al().Cloning and expression vector construction of full-length C4 type PEPC gene in maize[J].Jiangsu Agricultural Sciences,2014,42(10):26.
[10]雷恩,赵光明,刘艳红.不同稀释浓度松土保水剂对玉米营养生长的影响[J].江苏农业科学,2013,41(06):77.
　Lei En,et al.Effect of different dilutions of super absorbent polymer on vegetative growth of maize[J].Jiangsu Agricultural Sciences,2013,41(10):77.
[11]杨琳琳,别书凡,王建坤,等.基于深度学习的玉米植株表型检测方法研究[J].江苏农业科学,2023,51(19):165.
　Yang Linlin,et al.Study on phenotypic detection method for corn plants based on deep learning[J].Jiangsu Agricultural Sciences,2023,51(10):165.
[12]吕伟,宋轩,杨欢.基于深度学习和多源遥感数据的玉米种植面积提取[J].江苏农业科学,2023,51(23):171.
　Lyu Wei,et al.Extraction of maize planting area based on deep learning and multi-source remote sensing data[J].Jiangsu Agricultural Sciences,2023,51(10):171.
[13]贾海峰,王廷超,洪小丽,等.基于双解码机制的田间玉米冠层图像分割及昼夜节律解析[J].江苏农业科学,2023,51(24):184.
　Jia Haifeng,et al.Field maize canopy image segmentation and circadian rhythms analysis based on double decoding mechanism[J].Jiangsu Agricultural Sciences,2023,51(10):184.
[14]许爽,杨乐,刘婷.Ghost-MobileNet v2：一种轻量级玉米田杂草识别新模型[J].江苏农业科学,2024,52(20):173.
　Xu Shuang,et al.Ghost-MobileNet v2： a new lightweight identification model for weeds in maize field[J].Jiangsu Agricultural Sciences,2024,52(10):173.

备注/Memo

备注/Memo:
收稿日期：2023-08-09
基金项目：国家自然科学基金面上项目(编号：32072572)；河北省高层次人才资助项目(编号：E2019100006)；河北省重点研发计划(编号：20327403D)；河北农业大学引进人才科研专项(编号：YJ201847)。
作者简介：张友为(2000—)，男，河北沧州人，硕士研究生，主要从事深度学习研究。E-mail：1511313883@qq.com。
通信作者：范晓飞，博士，教授，从事智慧农业研究。E-mail：fanxiaofei@hebau.edu.cn。
更新日期/Last Update: 2024-05-20