Topic · Smart Agriculture and AI
Most cited papers in Smart Agriculture and AI
What are the most cited papers in Smart Agriculture and AI?
The most cited paper in Smart Agriculture and AI is "Deep learning in agriculture: A survey" (Andreas Kamilaris, Francesc Xavier Prenafeta-Boldú, 2018), with 5,070 citations. Next come "Using Deep Learning for Image-Based Plant Disease Detection" (4,980) and "Automated Flower Classification over a Large Number of Classes" (3,363).
Showing 50 of 100 papers. Every paper with more than 685 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep learning in agriculture: A survey Andreas Kamilaris, Francesc Xavier Prenafeta-Boldú · Computers and Electronics in Agriculture | 22 Feb 2018 | 5,070 |
| 2 | 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 |
| 3 | Automated Flower Classification over a Large Number of Classes Maria-Elena Nilsback, Andrew Zisserman | Dec 2008 | 3,363 |
| 4 | Deep learning models for plant disease detection and diagnosis Konstantinos P. Ferentinos · Computers and Electronics in Agriculture | Feb 2018 | 3,356 |
| 5 | Machine Learning in Agriculture: A Review Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou et al. · Sensors | 14 Aug 2018 | 3,332 |
| 6 | Food-101 – Mining Discriminative Components with Random Forests Lukas Bossard, Matthieu Guillaumin, Luc Van Gool · Lecture notes in computer science | 2014 | 2,104 |
| 7 | 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 |
| 8 | The application of small unmanned aerial systems for precision agriculture: a review Chunhua Zhang, John M. Kovacs · Precision Agriculture | 30 Jul 2012 | 2,022 |
| 9 | Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps David J. MULLA · Biosystems Engineering | 13 Sep 2012 | 1,980 |
| 10 | Crop yield prediction using machine learning: A systematic literature review Thomas van Klompenburg, Ayalew Kassahun, Cagatay Catal · Computers and Electronics in Agriculture | 18 Aug 2020 | 1,846 |
| 11 | Field high-throughput phenotyping: the new crop breeding frontier J. L. Araus, Jill E. Cairns · Trends in Plant Science | 16 Oct 2013 | 1,838 |
| 12 | Precision Agriculture and Food Security Robin Gebbers, Viacheslav I. Adamchuk · Science | 11 Feb 2010 | 1,597 |
| 13 | Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review Anna Chlingaryan, Salah Sukkarieh, Brett M. Whelan · Computers and Electronics in Agriculture | 5 Jun 2018 | 1,578 |
| 14 | A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition Alvaro Fuentes, Sook Yoon, Sang Ryong Kim et al. · Sensors | 4 Sep 2017 | 1,578 |
| 15 | The iNaturalist Species Classification and Detection Dataset Grant Van Horn, Oisin Mac Aodha, Yang Song et al. | Jun 2018 | 1,487 |
| 16 | An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges Olakunle Elijah, Tharek Abdul Rahman, Igbafe Orikumhi et al. · IEEE Internet of Things Journal | 5 Jun 2018 | 1,471 |
| 17 | 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 |
| 18 | Applications of Remote Sensing in Precision Agriculture: A Review Rajendra P. Sishodia, Ram L. Ray, Sudhir Kumar Singh · Remote Sensing | 24 Sep 2020 | 1,448 |
| 19 | Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping Anne‐Katrin Mahlein · Plant Disease | Sep 2015 | 1,365 |
| 20 | Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk Muhammad Ayaz, Mohammad Ammad-Uddin, Zubair Sharif et al. · IEEE Access | 2019 | 1,296 |
| 21 | Precision agriculture—a worldwide overview Naiqian Zhang, Maohua Wang, Ning Wang · Computers and Electronics in Agriculture | Nov 2002 | 1,296 |
| 22 | A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda Laurens Klerkx, Emma Jakku, Pierre Labarthe · NJAS - Wageningen Journal of Life Sciences | 19 Nov 2019 | 1,286 |
| 23 | Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture Bing Lu, Phuong D. Dao, Jiangui Liu et al. · Remote Sensing | 18 Aug 2020 | 1,231 |
| 24 | A Review on UAV-Based Applications for Precision Agriculture Dimosthenis C. Tsouros, Stamatia Bibi, Panagiotis G. Sarigiannidis · Information | 11 Nov 2019 | 1,220 |
| 25 | Detection of plant leaf diseases using image segmentation and soft computing techniques V. K. Singh, Archan Misra · Information Processing in Agriculture | 4 Nov 2016 | 1,197 |
| 26 | A review on the practice of big data analysis in agriculture Andreas Kamilaris, Andreas Kartakoullis, Francesc Xavier Prenafeta-Boldú · Computers and Electronics in Agriculture | 12 Oct 2017 | 1,175 |
| 27 | Machine Learning for High-Throughput Stress Phenotyping in Plants Arti Singh, Arti Singh, Baskar Ganapathysubramanian et al. · Trends in Plant Science | Dec 2015 | 1,137 |
| 28 | DeepFruits: A Fruit Detection System Using Deep Neural Networks Inkyu Sa, Zongyuan Ge, Feras Dayoub et al. · Sensors | 3 Aug 2016 | 1,120 |
| 29 | A Review of Imaging Techniques for Plant Phenotyping Lei Li, Qin Zhang, Danfeng Huang · Sensors | 24 Oct 2014 | 1,115 |
| 30 | Machine Learning Applications for Precision Agriculture: A Comprehensive Review Abhinav Sharma, Arpit Jain, Prateek Gupta et al. · IEEE Access | 31 Dec 2020 | 1,097 |
| 31 | Identification of rice diseases using deep convolutional neural networks Yang Lü, Shujuan Yi, Nianyin Zeng et al. · Neurocomputing | 30 Jun 2017 | 1,061 |
| 32 | Apple detection during different growth stages in orchards using the improved YOLO-V3 model Yunong Tian, Guodong Yang, Zhe Wang et al. · Computers and Electronics in Agriculture | 19 Jan 2019 | 1,054 |
| 33 | 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 |
| 34 | Verification of color vegetation indices for automated crop imaging applications George E. Meyer, João Camargo Neto · Computers and Electronics in Agriculture | 28 May 2008 | 1,040 |
| 35 | Deep learning based multi-temporal crop classification Liheng Zhong, Lina Hu, Hang Zhou · Remote Sensing of Environment | 3 Dec 2018 | 1,031 |
| 36 | Plant diseases and pests detection based on deep learning: a review Jun Liu, Xuewei Wang · Plant Methods | 24 Feb 2021 | 1,010 |
| 37 | Improving quality inspection of food products by computer vision––a review Tadhg Brosnan, Da‐Wen Sun · Journal of Food Engineering | 31 Jul 2003 | 1,009 |
| 38 | A comprehensive review on automation in agriculture using artificial intelligence Kirtan Jha, Aalap Doshi, Poojan Patel et al. · Artificial Intelligence in Agriculture | Jun 2019 | 1,005 |
| 39 | Plant Disease Detection and Classification by Deep Learning—A Review Lili Li, Shujuan Zhang, Bin Wang · IEEE Access | 2021 | 1,004 |
| 40 | Internet of Things in agriculture, recent advances and future challenges Antonis Tzounis, Nikolaos Katsoulas, Thomas Bartzanas et al. · Biosystems Engineering | 24 Nov 2017 | 991 |
| 41 | Soybean yield prediction from UAV using multimodal data fusion and deep learning Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike et al. · Remote Sensing of Environment | 17 Dec 2019 | 984 |
| 42 | From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management Verónica Sáiz-Rubio, Francisco Rovira-Más · Agronomy | 3 Feb 2020 | 978 |
| 43 | 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 |
| 44 | A compilation of UAV applications for precision agriculture Panagiotis I. Radoglou Grammatikis, Panagiotis G. Sarigiannidis, Θωμάς Λάγκας et al. · Computer Networks | 12 Feb 2020 | 964 |
| 45 | IoT Solutions in Agriculture: Enhancing Efficiency and Productivity Shradha Pawar · International Journal of Innovative Science and Research Technology (IJISRT) | 21 Jun 2024 | 962 |
| 46 | Deep Learning for Tomato Diseases: Classification and Symptoms Visualization Mohammed Brahimi, Kamel Boukhalfa, Abdelouahab Moussaouı · Applied Artificial Intelligence | 21 Apr 2017 | 951 |
| 47 | Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review Diego Patrício, Rafael Rieder · Computers and Electronics in Agriculture | 9 Aug 2018 | 946 |
| 48 | Plant leaf disease classification using EfficientNet deep learning model Ümit Atila, Murat Berat UÇAR, Kemal Akyol et al. · Ecological Informatics | 30 Oct 2020 | 944 |
| 49 | A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu et al. | Dec 2007 | 939 |
| 50 | Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides Tanha Talaviya, Dhara Shah, Nivedita Patel et al. · Artificial Intelligence in Agriculture | 2020 | 914 |
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.