| 1 | Deep learning in agriculture: A survey | Computers and Electronics in Agriculture | 2018 | 2,544 |
| 2 | Deep learning models for plant disease detection and diagnosis | Computers and Electronics in Agriculture | 2018 | 1,317 |
| 3 | Crop yield prediction using machine learning: A systematic literature review | Computers and Electronics in Agriculture | 2020 | 873 |
| 4 | Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review | Computers and Electronics in Agriculture | 2018 | 870 |
| 5 | A review of advanced techniques for detecting plant diseases | Computers and Electronics in Agriculture | 2010 | 733 |
| 6 | Verification of color vegetation indices for automated crop imaging applications | Computers and Electronics in Agriculture | 2008 | 726 |
| 7 | Apparent soil electrical conductivity measurements in agriculture | Computers and Electronics in Agriculture | 2005 | 694 |
| 8 | Apple detection during different growth stages in orchards using the improved YOLO-V3 model | Computers and Electronics in Agriculture | 2019 | 671 |
| 9 | A comparative study of fine-tuning deep learning models for plant disease identification | Computers and Electronics in Agriculture | 2019 | 640 |
| 10 | Precision agriculture—a worldwide overview | Computers and Electronics in Agriculture | 2002 | 625 |
| 11 | Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance | Computers and Electronics in Agriculture | 2010 | 564 |
| 12 | Soil properties influencing apparent electrical conductivity: a review | Computers and Electronics in Agriculture | 2005 | 553 |
| 13 | Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review | Computers and Electronics in Agriculture | 2018 | 545 |
| 14 | A review on the practice of big data analysis in agriculture | Computers and Electronics in Agriculture | 2017 | 526 |
| 15 | Using deep transfer learning for image-based plant disease identification | Computers and Electronics in Agriculture | 2020 | 499 |
| 16 | Decision support systems for agriculture 4.0: Survey and challenges | Computers and Electronics in Agriculture | 2020 | 442 |
| 17 | Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges | Computers and Electronics in Agriculture | 2015 | 441 |
| 18 | Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN | Computers and Electronics in Agriculture | 2019 | 441 |
| 19 | Wireless sensors in agriculture and food industry—Recent development and future perspective | Computers and Electronics in Agriculture | 2006 | 439 |
| 20 | Monitoring plant diseases and pests through remote sensing technology: A review | Computers and Electronics in Agriculture | 2019 | 434 |
| 21 | On-the-go soil sensors for precision agriculture | Computers and Electronics in Agriculture | 2004 | 419 |
| 22 | Autonomous robotic weed control systems: A review | Computers and Electronics in Agriculture | 2008 | 414 |
| 23 | Agricultural land use suitability analysis using GIS and AHP technique | Computers and Electronics in Agriculture | 2013 | 412 |
| 24 | A review on weed detection using ground-based machine vision and image processing techniques | Computers and Electronics in Agriculture | 2019 | 408 |
| 25 | Sensors and systems for fruit detection and localization: A review | Computers and Electronics in Agriculture | 2015 | 394 |
| 26 | Deep learning – Method overview and review of use for fruit detection and yield estimation | Computers and Electronics in Agriculture | 2019 | 393 |
| 27 | An overview of current and potential applications of thermal remote sensing in precision agriculture | Computers and Electronics in Agriculture | 2017 | 392 |
| 28 | A geometric solar radiation model with applications in agriculture and forestry | Computers and Electronics in Agriculture | 2002 | 391 |
| 29 | Drones in agriculture: A review and bibliometric analysis | Computers and Electronics in Agriculture | 2022 | 391 |
| 30 | Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification | Computers and Electronics in Agriculture | 2018 | 385 |
| 31 | Testing the performance of spatial interpolation techniques for mapping soil properties | Computers and Electronics in Agriculture | 2006 | 381 |
| 32 | A survey of image processing techniques for plant extraction and segmentation in the field | Computers and Electronics in Agriculture | 2016 | 370 |
| 33 | Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV | Computers and Electronics in Agriculture | 2014 | 347 |
| 34 | Tomato plant disease detection using transfer learning with C-GAN synthetic images | Computers and Electronics in Agriculture | 2021 | 347 |
| 35 | Deep learning for plant identification using vein morphological patterns | Computers and Electronics in Agriculture | 2016 | 343 |
| 36 | Shape identification and particles size distribution from basic shape parameters using ImageJ | Computers and Electronics in Agriculture | 2008 | 339 |
| 37 | Introducing digital twins to agriculture | Computers and Electronics in Agriculture | 2021 | 338 |
| 38 | Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture | Computers and Electronics in Agriculture | 2019 | 335 |
| 39 | IoT and agriculture data analysis for smart farm | Computers and Electronics in Agriculture | 2019 | 334 |
| 40 | Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments | Computers and Electronics in Agriculture | 2020 | 332 |
| 41 | Measurement of soil water content and electrical conductivity by time domain reflectometry: a review | Computers and Electronics in Agriculture | 2001 | 330 |
| 42 | Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size | Computers and Electronics in Agriculture | 2002 | 319 |
| 43 | Weed detection in soybean crops using ConvNets | Computers and Electronics in Agriculture | 2017 | 318 |
| 44 | Crop yield prediction with deep convolutional neural networks | Computers and Electronics in Agriculture | 2019 | 317 |
| 45 | State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: A review | Computers and Electronics in Agriculture | 2020 | 312 |
| 46 | Sensing technologies for precision specialty crop production | Computers and Electronics in Agriculture | 2010 | 305 |
| 47 | Wheat yield prediction using machine learning and advanced sensing techniques | Computers and Electronics in Agriculture | 2016 | 305 |
| 48 | An IoT based smart irrigation management system using Machine learning and open source technologies | Computers and Electronics in Agriculture | 2018 | 294 |
| 49 | Deep feature based rice leaf disease identification using support vector machine | Computers and Electronics in Agriculture | 2020 | 294 |
| 50 | Crop pest classification based on deep convolutional neural network and transfer learning | Computers and Electronics in Agriculture | 2019 | 293 |