Most cited papers in Plant Disease Management Techniques
What are the most cited papers in Plant Disease Management Techniques?
The most cited paper in Plant Disease Management Techniques is "Using Deep Learning for Image-Based Plant Disease Detection" (Sharada P. Mohanty, David Peter Hughes, Marcel Salathé, 2016), with 4,980 citations. Next come "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification" (2,026) and "M ICROBIAL P OPULATIONS R ESPONSIBLE FOR S PECIFIC S OIL S UPPRESSIVENESS TO P LANT P ATHOGENS" (1,702).
Showing 50 of 100 papers. Every paper with more than 355 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Using Deep Learning for Image-Based Plant Disease Detection Sharada P. Mohanty, David Peter Hughes, Marcel Salathé · Frontiers in Plant Science | 22 Sep 2016 | 4,980 |
| 2 | Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification Srdjan Sladojević, Marko Arsenović, Andraš Anderla et al. · Computational Intelligence and Neuroscience | 2016 | 2,026 |
| 3 | M ICROBIAL P OPULATIONS R ESPONSIBLE FOR S PECIFIC S OIL S UPPRESSIVENESS TO P LANT P ATHOGENS David M. Weller, Jos M. Raaijmakers, Brian B. McSpadden Gardener et al. · Annual Review of Phytopathology | Sep 2002 | 1,702 |
| 4 | Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions David M. Woebbecke, George E. Meyer, Kenneth Von Bargen et al. · Transactions of the ASAE | 1995 | 1,551 |
| 5 | USE OF ACID, ROSE BENGAL, AND STREPTOMYCIN IN THE PLATE METHOD FOR ESTIMATING SOIL FUNGI James P. Martin · Soil Science | Mar 1950 | 1,482 |
| 6 | A comparative study of fine-tuning deep learning models for plant disease identification Edna Chebet Too, Yujian Li, Sam Njuki et al. · Computers and Electronics in Agriculture | 30 Mar 2018 | 1,467 |
| 7 | Clay nanosheets for topical delivery of RNAi for sustained protection against plant viruses Neena Mitter, Elizabeth A. Worrall, Karl E. Robinson et al. · Nature Plants | 9 Jan 2017 | 1,096 |
| 8 | Using deep transfer learning for image-based plant disease identification Junde Chen, Jinxiu Chen, Defu Zhang et al. · Computers and Electronics in Agriculture | 6 Apr 2020 | 1,051 |
| 9 | Plant diseases and pests detection based on deep learning: a review Jun Liu, Xuewei Wang · Plant Methods | 24 Feb 2021 | 1,010 |
| 10 | Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance Till Rumpf, Anne‐Katrin Mahlein, Ulrike Steiner et al. · Computers and Electronics in Agriculture | 22 Aug 2010 | 983 |
| 11 | Using Machine Learning to Identify Diseases and Perform Sorting in Apple Fruit Arpit Patidar, Abir Chakravorty · International Journal of Innovative Science and Research Technology (IJISRT) | 25 Jul 2024 | 964 |
| 12 | Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks Peng Jiang, Yuehan Chen, Bin Liu et al. · IEEE Access | 2019 | 892 |
| 13 | In Vitro Propagation of Paradox Walnut Rootstock John A. Driver, Andrew H. Kuniyuki · HortScience | Aug 1984 | 875 |
| 14 | First Report of the Cereal Cyst Nematode Heterodera latipons on Wheat in Morocco Fouad Mokrını, Lieven Waeyenberge, Nicole M. Viaene et al. · Plant Disease | 15 Feb 2012 | 859 |
| 15 | The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions Shaogui Guo et al. · Nature Genetics | 25 Nov 2012 | 833 |
| 16 | Current status of vegetable grafting: Diffusion, grafting techniques, automation Jung-Myung Lee, C. Kubota, Shing-Jy Tsao et al. · Scientia Horticulturae | 25 Sep 2010 | 819 |
| 17 | Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks Bin Liu, Yun Zhang, Dongjian He et al. · Symmetry | 29 Dec 2017 | 803 |
| 18 | 50-plus years of fungal viruses Said A. Ghabrial, José R. Castón, Dàohóng Jiāng et al. · Virology | 13 Mar 2015 | 791 |
| 19 | Plant disease identification from individual lesions and spots using deep learning Jayme Garcia Arnal Barbedo · Biosystems Engineering | 14 Feb 2019 | 783 |
| 20 | Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning Guan Wang, Yu Sun, Jianxin Wang · Computational Intelligence and Neuroscience | 2017 | 748 |
| 21 | Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification Jayme Garcia Arnal Barbedo · Computers and Electronics in Agriculture | 9 Aug 2018 | 740 |
| 22 | Exploitation of natural products as an alternative strategy to control postharvest fungal rotting of fruit and vegetables P. N. Tripathi, Nawal Kishore Dubey · Postharvest Biology and Technology | May 2004 | 739 |
| 23 | The influence of temperature and moisture contents regimes on the aerobic microbial activity of a biosolids composting blend Cuiyi Liang, K. C. Das, R. W. McClendon · Bioresource Technology | 16 Oct 2002 | 701 |
| 24 | Plant genotype affects total antioxidant capacity and phenolic contents in fruit Jessica Scalzo, Alessia Politi, Nicoletta Pellegrini et al. · Nutrition | Feb 2005 | 694 |
| 25 | The effectiveness of plant essential oils on the growth of Botrytis cinerea, Fusarium sp. and Clavibacter michiganensis subsp. michiganensis Dimitra J. Daferera, Basil N. Ziogas, Moschos G. Polissiou · Crop Protection | 3 Dec 2002 | 682 |
| 26 | The Date Palm (Phoenix dactylifera L.): Overview of Biology, Uses, and Cultivation Chih-Cheng T. Chao, Robert R. Krueger · HortScience | Aug 2007 | 675 |
| 27 | PlantDoc Davinder Singh, Naman Jain, Pranjali Jain et al. | 5 Jan 2020 | 668 |
| 28 | Threats to global food security from emerging fungal and oomycete crop pathogens Helen N. Fones, Daniel P. Bebber, Thomas M. Chaloner et al. · Nature Food | 8 Jun 2020 | 659 |
| 29 | Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks Xihai Zhang, Yue Qiao, Fanfeng Meng et al. · IEEE Access | 2018 | 659 |
| 30 | Tomato plant disease detection using transfer learning with C-GAN synthetic images Amreen Abbas, Sweta Jain, Mahesh Gour et al. · Computers and Electronics in Agriculture | 29 Jun 2021 | 655 |
| 31 | Factors influencing the use of deep learning for plant disease recognition Jayme Garcia Arnal Barbedo · Biosystems Engineering | 17 Jun 2018 | 636 |
| 32 | An inter-laboratory comparison of ten different ways of measuring soil microbial biomass C T. H. Beck, Rainer Georg Joergensen, Ellen Kandeler et al. · Soil Biology and Biochemistry | Jul 1997 | 630 |
| 33 | Sources and contamination routes of microbial pathogens to fresh produce during field cultivation: A review Oluwadara Oluwaseun Alegbeleye, Ian Singleton, Anderson S. Sant’Ana · Food Microbiology | 3 Feb 2018 | 623 |
| 34 | Suppressing soil-borne diseases with residue management and organic amendments K. L. Bailey, George Lazarovits · Soil and Tillage Research | 21 Jun 2003 | 606 |
| 35 | Weed detection in soybean crops using ConvNets Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva et al. · Computers and Electronics in Agriculture | 12 Nov 2017 | 584 |
| 36 | Grafting as a tool to improve tolerance of vegetables to abiotic stresses: Thermal stress, water stress and organic pollutants Dietmar Schwarz, Youssef Rouphael, Giuseppe Colla et al. · Scientia Horticulturae | 17 Oct 2010 | 584 |
| 37 | A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5 Bin Yan, Pan Fan, Xiaoyan Lei et al. · Remote Sensing | 21 Apr 2021 | 582 |
| 38 | Impact of soil health management practices on soilborne pathogens, nematodes and root diseases of vegetable crops G. S. Abawi, Timothy L. Widmer · Applied Soil Ecology | Aug 2000 | 579 |
| 39 | A geminivirus-related DNA mycovirus that confers hypovirulence to a plant pathogenic fungus Xiao Yu, Bo Li, Yànpíng Fù et al. · Proceedings of the National Academy of Sciences | 19 Apr 2010 | 578 |
| 40 | Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild Artzai Picón, Aitor Álvarez-Gila, Maximiliam Seitz et al. · Computers and Electronics in Agriculture | 21 Apr 2018 | 571 |
| 41 | A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network Juncheng Ma, Keming Du, Feixiang Zheng et al. · Computers and Electronics in Agriculture | 6 Sep 2018 | 564 |
| 42 | Cultivation of Grafted Vegetables I. Current Status, Grafting Methods, and Benefits Jung-Myung Lee · HortScience | Apr 1994 | 557 |
| 43 | Determination of kC and kNin situ for calibration of the chloroform fumigation-incubation method R. Paul Voroney, Eldor A. Paul · Soil Biology and Biochemistry | 1984 | 522 |
| 44 | Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection Marko Arsenović, Mirjana Karanovic, Srdjan Sladojević et al. · Symmetry | 19 Jul 2019 | 520 |
| 45 | Chitosan Coating Effect on Storability and Quality of Fresh Strawberries Ahmed El Ghaouth, Joseph Arul, Rathy Ponnampalam et al. · Journal of Food Science | Nov 1991 | 509 |
| 46 | Recent Trends in Control Methods for Bacterial Wilt Diseases Caused by Ralstonia solanacearum Yuliar Yuliar, Yanetri Asi Nion, Koki Toyota · Microbes and Environments | 2015 | 508 |
| 47 | Charcoal Volatile Matter Content Influences Plant Growth and Soil Nitrogen Transformations Jonathan L. Deenik, Tai McClellan, G. Uehara et al. · Soil Science Society of America Journal | 20 Jun 2010 | 500 |
| 48 | NATURE OF AVERAGE EVOKED POTENTIALS TO SOUND AND OTHER STIMULI IN MAN* Reginald G. Bickford, James L. Jacobson, D. Thane R. Cody · Annals of the New York Academy of Sciences | May 1964 | 500 |
| 49 | Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach Sk Mahmudul Hassan, Arnab Kumar Maji, Michał Jasiński et al. · Electronics | 9 Jun 2021 | 497 |
| 50 | Refinement of the Triphenyl Tetrazolium Chloride Method of Determining Cold Injury Peter L. Steponkus, F. O. Lanphear · PLANT PHYSIOLOGY | Oct 1967 | 496 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.