OOIR: Observatory of International Research

Papers

(The H4-Index of Computers and Electronics in Agriculture is 74. 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 2020-11-01 to 2024-11-01.)

Article	Citations
Tomato plant disease detection using transfer learning with C-GAN synthetic images	320
Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments	309
Introducing digital twins to agriculture	277
A survey of deep learning techniques for weed detection from images	260
Drones in agriculture: A review and bibliometric analysis	223
Image recognition of four rice leaf diseases based on deep learning and support vector machine	220
A survey on the 5G network and its impact on agriculture: Challenges and opportunities	211
Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges	207
Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming	197
A review on plant high-throughput phenotyping traits using UAV-based sensors	190
An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease	175
A survey of public datasets for computer vision tasks in precision agriculture	171
Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network	155
Detection and classification of soybean pests using deep learning with UAV images	146
Do we really need deep CNN for plant diseases identification?	145
Plant diseases recognition on images using convolutional neural networks: A systematic review	142
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices	141
A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net	137
A systematic literature review on the use of machine learning in precision livestock farming	136
An overview of agriculture 4.0 development: Systematic review of descriptions, technologies, barriers, advantages, and disadvantages	128
Review of the internet of things communication technologies in smart agriculture and challenges	119
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review	118
Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning	118
Grape disease image classification based on lightweight convolution neural networks and channelwise attention	112
Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet	112

Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications	112
Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4	111
Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks	111
A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping	111
Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4	106
A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images	104
Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++	104
Meta-learning baselines and database for few-shot classification in agriculture	104
3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM	103
Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture	102
Applications of IoT for optimized greenhouse environment and resources management	100
Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery	99
Applications of machine vision in agricultural robot navigation: A review	98
UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages	98
Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning	97
A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves	96
Classification of rice varieties with deep learning methods	94
Corn cash price forecasting with neural networks	94
Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review	94
Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN	92
A deep learning approach incorporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings	92
Multi-step ahead forecasting of daily reference evapotranspiration using deep learning	90
Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration	89
Deep diagnosis: A real-time apple leaf disease detection system based on deep learning	88
Lightweight convolutional neural network model for field wheat ear disease identification	88
MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks	87
Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning	86
Detection and classification of tea buds based on deep learning	86
Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems	85
Technological revolutions in smart farming: Current trends, challenges & future directions	85
Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network	85
Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation	85
RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism	84
Strengthening consumer trust in beef supply chain traceability with a blockchain-based human-machine reconcile mechanism	84
RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification	84
Tomato leaf segmentation algorithms for mobile phone applications using deep learning	84
Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data	81
Pose estimation and behavior classification of broiler chickens based on deep neural networks	81
Support Vector Machine in Precision Agriculture: A review	81
Retinex-inspired color correction and detail preserved fusion for underwater image enhancement	80
YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems	79
Citrus pests classification using an ensemble of deep learning models	78
Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse	78
Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions	76
Close-range hyperspectral imaging of whole plants for digital phenotyping: Recent applications and illumination correction approaches	76
Estimation of corn yield based on hyperspectral imagery and convolutional neural network	76
A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN	75
Fusion of Mask RCNN and attention mechanism for instance segmentation of apples under complex background	75
A new attention-based CNN approach for crop mapping using time series Sentinel-2 images	75
A detection and severity estimation system for generic diseases of tomato greenhouse plants	74

Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes	74
A modified U-Net with a specific data argumentation method for semantic segmentation of weed images in the field	74