OOIR: Observatory of International Research

Papers

(The H4-Index of Computers and Electronics in Agriculture is 75. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)

Article	Citations
Crop yield prediction using machine learning: A systematic literature review	642
Using deep transfer learning for image-based plant disease identification	509
Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments	276
Tomato plant disease detection using transfer learning with C-GAN synthetic images	268
Deep feature based rice leaf disease identification using support vector machine	261
Introducing digital twins to agriculture	223
A survey of deep learning techniques for weed detection from images	213
State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: A review	211
Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot	196
A survey on the 5G network and its impact on agriculture: Challenges and opportunities	187
Few-Shot Learning approach for plant disease classification using images taken in the field	185
Image recognition of four rice leaf diseases based on deep learning and support vector machine	184
Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges	183
An optimized dense convolutional neural network model for disease recognition and classification in corn leaf	183
Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN	176
A review of computer vision technologies for plant phenotyping	164
Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review	161
A review on plant high-throughput phenotyping traits using UAV-based sensors	160
A review on monitoring and advanced control strategies for precision irrigation	156
Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach	154
Systematic literature review of implementations of precision agriculture	151
Multiclass classification of dry beans using computer vision and machine learning techniques	151
Drones in agriculture: A review and bibliometric analysis	144
A survey of public datasets for computer vision tasks in precision agriculture	139
An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease	138

Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network	137
Do we really need deep CNN for plant diseases identification?	132
Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming	128
Detection and classification of soybean pests using deep learning with UAV images	126
CNN feature based graph convolutional network for weed and crop recognition in smart farming	125
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices	121
Plant diseases recognition on images using convolutional neural networks: A systematic review	117
Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence	114
A systematic literature review on the use of machine learning in precision livestock farming	112
A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net	107
AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection	102
Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation	101
SoyNet: Soybean leaf diseases classification	100
An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages	100
Meta-learning baselines and database for few-shot classification in agriculture	100
Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning	99
Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4	98
Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data	97
Review of the internet of things communication technologies in smart agriculture and challenges	97
Grape disease image classification based on lightweight convolution neural networks and channelwise attention	96
Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture	95
Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications	93
An adaptive pig face recognition approach using Convolutional Neural Networks	92
Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks	92
3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM	92
Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse	91
Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet	89
A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images	88
Wireless technologies for smart agricultural monitoring using internet of things devices with energy harvesting capabilities	87
A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping	86
Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4	86
Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning	85
Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery	84
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review	83
Crop leaf disease recognition based on Self-Attention convolutional neural network	83
Terahertz spectroscopy and imaging: A review on agricultural applications	82
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning	82
Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN	80
Lightweight convolutional neural network model for field wheat ear disease identification	79
Classification of rice varieties with deep learning methods	79
Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology	79
Applications of IoT for optimized greenhouse environment and resources management	78
Automatically detecting pig position and posture by 2D camera imaging and deep learning	78
A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves	78
RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification	77
An automatic method for weed mapping in oat fields based on UAV imagery	76
Detection and classification of tea buds based on deep learning	76
Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning	76
Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration	76
Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease	75

Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++	75
Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset	75