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Systematic Review on Machine Learning and Computer Vision Methodology for Precision Agriculture Applications

https://doi.org/10.2139/ssrn.4836392
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic visual estimation of tomato cluster maturity in plant rows
inferredcites →https://doi.org/10.1007/s00371-021-02116-3openalex-snapshot
Automatic detection of oil palm fruits from UAV images using an improved YOLO model
inferredcites →https://doi.org/10.1007/s10661-022-10425-wopenalex-snapshot
Vegetation detection using vegetation indices algorithm supported by statistical machine learning
inferredcites →https://doi.org/10.1007/s11119-018-9614-1openalex-snapshot
Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery
inferredcites →https://doi.org/10.1007/s11119-020-09736-0openalex-snapshot
Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera
inferredcites →https://doi.org/10.1007/s11119-020-09771-xopenalex-snapshot
Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence
inferredcites →https://doi.org/10.1007/s11119-020-09777-5openalex-snapshot
Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery
inferredcites →https://doi.org/10.1007/s11119-022-09924-0openalex-snapshot
Mapping coffee yield with computer vision
inferredcites →https://doi.org/10.1007/s13244-018-0639-9openalex-snapshot
Convolutional neural networks: an overview and application in radiology
inferredcites →https://doi.org/10.1007/s44196-022-00129-xopenalex-snapshot
An Efficient Plant Disease Recognition System Using Hybrid Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for Smart IoT Applications in Agriculture
inferredcites →https://doi.org/10.1016/j.aej.2021.06.053openalex-snapshot
Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
inferredcites →https://doi.org/10.1016/j.aiia.2021.01.003openalex-snapshot
Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images
inferredcites →https://doi.org/10.1016/j.aiia.2021.03.001openalex-snapshot
An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields
inferredcites →https://doi.org/10.1016/j.aiia.2022.01.002openalex-snapshot
Deep convolutional neural network models for weed detection in polyhouse grown bell peppers
inferredcites →https://doi.org/10.1016/j.atech.2021.100030openalex-snapshot
Evaluation of cameras and image distance for CNN-based weed detection in wild blueberry
inferredcites →https://doi.org/10.1016/j.biosystemseng.2015.12.015openalex-snapshot
In-field automatic observation of wheat heading stage using computer vision
inferredcites →https://doi.org/10.1016/j.biosystemseng.2017.06.021openalex-snapshot
Machine vision system for the automatic segmentation of plants under different lighting conditions
inferredcites →https://doi.org/10.1016/j.biosystemseng.2020.08.015openalex-snapshot
Active thermal imaging for immature citrus fruit detection
inferredcites →https://doi.org/10.1016/j.compag.2018.04.002openalex-snapshot
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
inferredcites →https://doi.org/10.1016/j.compag.2018.10.006openalex-snapshot
Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images
inferredcites →https://doi.org/10.1016/j.compag.2018.11.005openalex-snapshot
Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers
inferredcites →https://doi.org/10.1016/j.compag.2018.12.048openalex-snapshot
Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence
inferredcites →https://doi.org/10.1016/j.compag.2019.01.030openalex-snapshot
Assessing Nitrogen and water status of winter wheat using a digital camera
inferredcites →https://doi.org/10.1016/j.compag.2019.105093openalex-snapshot
Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
inferredcites →https://doi.org/10.1016/j.compag.2019.105118openalex-snapshot
Multimodal neural network with clustering-based drop for estimating plant water stress
inferredcites →https://doi.org/10.1016/j.compag.2020.105247openalex-snapshot
Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
inferredcites →https://doi.org/10.1016/j.compag.2020.105306openalex-snapshot
Towards weeds identification assistance through transfer learning
inferredcites →https://doi.org/10.1016/j.compag.2020.105342openalex-snapshot
SoyNet: Soybean leaf diseases classification
inferredcites →https://doi.org/10.1016/j.compag.2020.105533openalex-snapshot
Generalizable semi-supervised learning method to estimate mass from sparsely annotated images
inferredcites →https://doi.org/10.1016/j.compag.2020.105788openalex-snapshot
Tomato leaf segmentation algorithms for mobile phone applications using deep learning
inferredcites →https://doi.org/10.1016/j.compag.2020.105796openalex-snapshot
Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions
inferredcites →https://doi.org/10.1016/j.compag.2020.105951openalex-snapshot
Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4
inferredcites →https://doi.org/10.1016/j.compag.2020.105964openalex-snapshot
Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve
inferredcites →https://doi.org/10.1016/j.compag.2021.105998openalex-snapshot
A deep learning approach for anthracnose infected trees classification in walnut orchards
inferredcites →https://doi.org/10.1016/j.compag.2021.106040openalex-snapshot
Application of deep learning to detect Lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada
inferredcites →https://doi.org/10.1016/j.compag.2021.106066openalex-snapshot
Detection, classification, and mapping of coffee fruits during harvest with computer vision
inferredcites →https://doi.org/10.1016/j.compag.2021.106132openalex-snapshot
Design, development, and performance evaluation of a robot for yield estimation of kiwifruit
inferredcites →https://doi.org/10.1016/j.compag.2021.106153openalex-snapshot
Medial axis-based machine-vision system for orchard robot navigation
inferredcites →https://doi.org/10.1016/j.compag.2021.106253openalex-snapshot
An industrial-grade solution for agricultural image classification tasks
inferredcites →https://doi.org/10.1016/j.compag.2021.106451openalex-snapshot
Instance segmentation of root crops and simulation-based learning to estimate their physical dimensions for on-line machine vision yield monitoring
inferredcites →https://doi.org/10.1016/j.compag.2021.106499openalex-snapshot
Improved digital image-based assessment of soil aggregate size by applying convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compag.2021.106543openalex-snapshot
An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network
inferredcites →https://doi.org/10.1016/j.compag.2021.106556openalex-snapshot
Smart tree crop sprayer utilizing sensor fusion and artificial intelligence
inferredcites →https://doi.org/10.1016/j.compag.2021.106606openalex-snapshot
Crop stem detection and tracking for precision hoeing using deep learning
inferredcites →https://doi.org/10.1016/j.compag.2022.106933openalex-snapshot
Insect counting through deep learning-based density maps estimation
inferredcites →https://doi.org/10.1016/j.compag.2022.107116openalex-snapshot
A YOLOv3-based computer vision system for identification of tea buds and the picking point
inferredcites →https://doi.org/10.1016/j.compag.2022.107119openalex-snapshot
Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
inferredcites →https://doi.org/10.1016/j.compag.2022.107269openalex-snapshot
Computer vision-based platform for apple leaves segmentation in field conditions to support digital phenotyping
inferredcites →https://doi.org/10.1016/j.compag.2022.107325openalex-snapshot
AI-enabled droplet detection and tracking for agricultural spraying systems
inferredcites →https://doi.org/10.1016/j.compag.2022.107444openalex-snapshot
Vision on the bog: Cranberry crop risk evaluation with deep learning
inferredcites →https://doi.org/10.1016/j.compeleceng.2022.108399openalex-snapshot
Artificial intelligence-enabled coconut tree disease detection and classification model for smart agriculture
inferredcites →https://doi.org/10.1016/j.compeleceng.2022.108411openalex-snapshot
Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment
inferredcites →https://doi.org/10.1016/j.compind.2018.03.010openalex-snapshot
Apple flower detection using deep convolutional networks
inferredcites →https://doi.org/10.1016/j.ecoinf.2022.101678openalex-snapshot
A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
inferredcites →https://doi.org/10.1016/j.eswa.2022.118117openalex-snapshot
Trends in vision-based machine learning techniques for plant disease identification: A systematic review
inferredcites →https://doi.org/10.1016/j.ifacol.2019.12.482openalex-snapshot
Automatic Detection of Tulip Breaking Virus (TBV) Using a Deep Convolutional Neural Network
inferredcites →https://doi.org/10.1016/j.inpa.2019.12.002openalex-snapshot
Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network
inferredcites →https://doi.org/10.1016/j.jafr.2022.100325openalex-snapshot
Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
inferredcites →https://doi.org/10.1016/j.matpr.2021.11.398openalex-snapshot
Application of machine learning techniques in rice leaf disease detection
inferredcites →https://doi.org/10.1016/j.measurement.2021.110669openalex-snapshot
Developing an automated monitoring system for fast and accurate prediction of soil texture using an image-based deep learning network and machine vision system
inferredcites →https://doi.org/10.1016/j.procs.2020.03.236openalex-snapshot
Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.155066openalex-snapshot
RETRACTED: A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm
inferredcites →https://doi.org/10.1038/s41438-019-0151-5openalex-snapshot
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production
inferredcites →https://doi.org/10.1073/pnas.1716999115openalex-snapshot
An explainable deep machine vision framework for plant stress phenotyping
inferredcites →https://doi.org/10.1080/23311916.2021.2018791openalex-snapshot
Classification of paddy crop and weeds using semantic segmentation
inferredcites →https://doi.org/10.1093/gigascience/giac054openalex-snapshot
Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision
inferredcites →https://doi.org/10.1109/access.2018.2812999openalex-snapshot
Modern Trends in Hyperspectral Image Analysis: A Review
inferredcites →https://doi.org/10.1109/access.2018.2851376openalex-snapshot
Automatic Detection of Field-Grown Cucumbers for Robotic Harvesting
inferredcites →https://doi.org/10.1109/access.2019.2907383openalex-snapshot
Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease
inferredcites →https://doi.org/10.1109/access.2019.2908846openalex-snapshot
Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study
inferredcites →https://doi.org/10.1109/access.2019.2960873openalex-snapshot
CRowNet: Deep Network for Crop Row Detection in UAV Images
inferredcites →https://doi.org/10.1109/access.2020.2991354openalex-snapshot
Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination
inferredcites →https://doi.org/10.1109/access.2020.3048415openalex-snapshot
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
inferredcites →https://doi.org/10.1109/access.2021.3057912openalex-snapshot
Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/access.2021.3086269openalex-snapshot
Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features
inferredcites →https://doi.org/10.1109/access.2021.3127881openalex-snapshot
A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques
inferredcites →https://doi.org/10.1109/access.2021.3135536openalex-snapshot
Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization
inferredcites →https://doi.org/10.1109/access.2022.3167061openalex-snapshot
Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN
inferredcites →https://doi.org/10.1109/isaiam53259.2021.00029openalex-snapshot
Classification of nutrient deficiency in rice based on CNN model with Reinforcement Learning augmentation
inferredcites →https://doi.org/10.1109/jetcas.2021.3101740openalex-snapshot
Automated Pest Detection With DNN on the Edge for Precision Agriculture
inferredcites →https://doi.org/10.1109/jmass.2021.3067102openalex-snapshot
Efficiently Mapping Large Areas of Olive Trees Using Drones in Extremadura, Spain
inferredcites →https://doi.org/10.1109/jrfid.2022.3206841openalex-snapshot
Cotton Disease Detection Based on ConvNeXt and Attention Mechanisms
inferredcites →https://doi.org/10.1109/jsen.2019.2935812openalex-snapshot
Designing Future Precision Agriculture: Detection of Seeds Germination Using Artificial Intelligence on a Low-Power Embedded System
inferredcites →https://doi.org/10.1109/jsen.2021.3056957openalex-snapshot
A Smart Droplet Detection Approach With Vision Sensing Technique for Agricultural Aviation Application
inferredcites →https://doi.org/10.1109/jsen.2021.3071290openalex-snapshot
AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture
inferredcites →https://doi.org/10.1109/jsen.2021.3129173openalex-snapshot
LEDNet: Deep Learning-Based Ground Sensor Data Monitoring System
inferredcites →https://doi.org/10.1109/jstars.2019.2902375openalex-snapshot
Geo-Object-Based Soil Organic Matter Mapping Using Machine Learning Algorithms With Multi-Source Geo-Spatial Data
inferredcites →https://doi.org/10.1109/jstars.2021.3118707openalex-snapshot
Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression
inferredcites →https://doi.org/10.1109/jstars.2021.3132228openalex-snapshot
Evaluation of Crop Health Status With UAS Multispectral Imagery
inferredcites →https://doi.org/10.1109/jstars.2022.3206399openalex-snapshot
YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning
inferredcites →https://doi.org/10.1109/lgrs.2019.2932385openalex-snapshot
Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/lgrs.2019.2954735openalex-snapshot
A Deep-Learning Approach for Automatic Counting of Soybean Insect Pests
inferredcites →https://doi.org/10.1109/lgrs.2022.3198850openalex-snapshot
Phenotypic Parameters Estimation of Plants Using Deep Learning-Based 3-D Reconstruction From Single RGB Image
inferredcites →https://doi.org/10.1109/lra.2022.3188105openalex-snapshot
Explicitly Incorporating Spatial Information to Recurrent Networks for Agriculture
inferredcites →https://doi.org/10.1109/tase.2020.3022868openalex-snapshot
A Methodology for the Detection of Nitrogen Deficiency in Corn Fields Using High-Resolution RGB Imagery
inferredcites →https://doi.org/10.1109/tgrs.2021.3134564openalex-snapshot
Toward Crop Maturity Assessment via UAS-Based Imaging Spectroscopy—A Snap Bean Pod Size Classification Field Study
inferredcites →https://doi.org/10.1109/tii.2020.2979237openalex-snapshot
Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring
inferredcites →https://doi.org/10.1109/tii.2020.3009736openalex-snapshot
Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-Enabled Dynamic Model
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1136/bmj.n71openalex-snapshot
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.1155/2022/1598796openalex-snapshot
Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection
inferredcites →https://doi.org/10.1155/2022/6534238openalex-snapshot
Machine Learning Technique for Precision Agriculture Applications in 5G‐Based Internet of Things
inferredcites →https://doi.org/10.11591/ijai.v9.i4.pp670-683openalex-snapshot
Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
inferredcites →https://doi.org/10.1186/s13007-020-00582-9openalex-snapshot
SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging
inferredcites →https://doi.org/10.1186/s13638-019-1613-zopenalex-snapshot
Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system
inferredcites →https://doi.org/10.1201/b18759-2openalex-snapshot
Historical Evolution and Recent Advances in Precision Farming
inferredcites →https://doi.org/10.13189/ujar.2022.100502openalex-snapshot
Analysis of Methods of Machine Learning Techniques for Detection and Classification of Brown Spot (Rice) Disease
inferredcites →https://doi.org/10.14569/ijacsa.2021.0121234openalex-snapshot
A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks
inferredcites →https://doi.org/10.14569/ijacsa.2022.0130795openalex-snapshot
Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things
inferredcites →https://doi.org/10.1590/1678-992x-2022-0064openalex-snapshot
Detection of coffee fruits on tree branches using computer vision
inferredcites →https://doi.org/10.1613/jair.301openalex-snapshot
Reinforcement Learning: A Survey
inferredcites →https://doi.org/10.18178/ijmerr.10.7.374-385openalex-snapshot
Vision-based Vineyard Trunk Detection and its Integration into a Grapes Harvesting Robot
inferredcites →https://doi.org/10.18280/ria.360102openalex-snapshot
Weed Detection in Pea Cultivation with the Faster RCNN ResNet 50 Convolutional Neural Network
inferredcites →https://doi.org/10.2478/ata-2021-0008openalex-snapshot
Artificial Intelligence-Driven Autonomous Robot for Precision Agriculture
inferredcites →https://doi.org/10.26656/fr.2017.4(s6).029openalex-snapshot
Lettuce classification using convolutional neural network
inferredcites →https://doi.org/10.31222/osf.io/v7gm2openalex-snapshot
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.32604/cmc.2021.012431openalex-snapshot
Paddy Leaf Disease Detection Using an Optimized Deep Neural Network
inferredcites →https://doi.org/10.32604/cmc.2021.018758openalex-snapshot
Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset
inferredcites →https://doi.org/10.32604/cmc.2022.021299openalex-snapshot
Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture
inferredcites →https://doi.org/10.32604/cmc.2022.028504openalex-snapshot
Rice Disease Diagnosis System (RDDS)
inferredcites →https://doi.org/10.3389/fpls.2019.00611openalex-snapshot
Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree
inferredcites →https://doi.org/10.3389/fpls.2021.705021openalex-snapshot
Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point
inferredcites →https://doi.org/10.3389/fpls.2022.906751openalex-snapshot
Automatic and Accurate Acquisition of Stem-Related Phenotypes of Mature Soybean Based on Deep Learning and Directed Search Algorithms
inferredcites →https://doi.org/10.3389/fpls.2022.969205openalex-snapshot
Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset
inferredcites →https://doi.org/10.3389/frobt.2021.627009openalex-snapshot
Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels
inferredcites →https://doi.org/10.3390/a15120440openalex-snapshot
Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
inferredcites →https://doi.org/10.3390/agriculture11040312openalex-snapshot
Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
inferredcites →https://doi.org/10.3390/agriculture11111049openalex-snapshot
Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm
inferredcites →https://doi.org/10.3390/agriculture12081226openalex-snapshot
Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique
inferredcites →https://doi.org/10.3390/agriculture12091408openalex-snapshot
Influence of Selected Modeling Parameters on Plant Segmentation Quality Using Decision Tree Classifiers
inferredcites →https://doi.org/10.3390/agriculture12101529openalex-snapshot
Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops
inferredcites →https://doi.org/10.3390/agriculture12122137openalex-snapshot
Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing
inferredcites →https://doi.org/10.3390/agriengineering2030032openalex-snapshot
A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images
inferredcites →https://doi.org/10.3390/agriengineering3020020openalex-snapshot
Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques
inferredcites →https://doi.org/10.3390/agronomy11112353openalex-snapshot
Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/agronomy11122440openalex-snapshot
FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards
inferredcites →https://doi.org/10.3390/agronomy12050981openalex-snapshot
Prediction of Strawberry Leaf Color Using RGB Mean Values Based on Soil Physicochemical Parameters Using Machine Learning Models
inferredcites →https://doi.org/10.3390/agronomy12051167openalex-snapshot
Novel Assessment of Region-Based CNNs for Detecting Monocot/Dicot Weeds in Dense Field Environments
inferredcites →https://doi.org/10.3390/agronomy12102463openalex-snapshot
Computer Vision and Deep Learning for Precision Viticulture
inferredcites →https://doi.org/10.3390/agronomy12102551openalex-snapshot
Design and Experimental Verification of the YOLOV5 Model Implanted with a Transformer Module for Target-Oriented Spraying in Cabbage Farming
inferredcites →https://doi.org/10.3390/app11157151openalex-snapshot
Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing
inferredcites →https://doi.org/10.3390/app12115650openalex-snapshot
Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery
inferredcites →https://doi.org/10.3390/app12189371openalex-snapshot
Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
inferredcites →https://doi.org/10.3390/app122211420openalex-snapshot
Smart Agriculture: A Fruit Flower Cluster Detection Strategy in Apple Orchards Using Machine Vision and Learning
inferredcites →https://doi.org/10.3390/app122412828openalex-snapshot
Modified Barnacles Mating Optimization with Deep Learning Based Weed Detection Model for Smart Agriculture
inferredcites →https://doi.org/10.3390/drones5010004openalex-snapshot
Automated Agave Detection and Counting Using a Convolutional Neural Network and Unmanned Aerial Systems
inferredcites →https://doi.org/10.3390/electronics8030292openalex-snapshot
A State-of-the-Art Survey on Deep Learning Theory and Architectures
inferredcites →https://doi.org/10.3390/fi14110323openalex-snapshot
Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application
inferredcites →https://doi.org/10.3390/jsan10010015openalex-snapshot
Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves
inferredcites →https://doi.org/10.3390/plants11060768openalex-snapshot
Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning
inferredcites →https://doi.org/10.3390/rs10111690openalex-snapshot
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
inferredcites →https://doi.org/10.3390/rs11040410openalex-snapshot
UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence
inferredcites →https://doi.org/10.3390/rs12111838openalex-snapshot
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms
inferredcites →https://doi.org/10.3390/rs12182977openalex-snapshot
Detection of Italian Ryegrass in Wheat and Prediction of Competitive Interactions Using Remote-Sensing and Machine-Learning Techniques
inferredcites →https://doi.org/10.3390/rs12183015openalex-snapshot
Mask R-CNN Refitting Strategy for Plant Counting and Sizing in UAV Imagery
inferredcites →https://doi.org/10.3390/rs12213521openalex-snapshot
An Automated Framework for Plant Detection Based on Deep Simulated Learning from Drone Imagery
inferredcites →https://doi.org/10.3390/rs13010023openalex-snapshot
Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection
inferredcites →https://doi.org/10.3390/rs13050858openalex-snapshot
Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping
inferredcites →https://doi.org/10.3390/rs13142705openalex-snapshot
Mapping Potato Plant Density Variation Using Aerial Imagery and Deep Learning Techniques for Precision Agriculture
inferredcites →https://doi.org/10.3390/rs13163191openalex-snapshot
How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows
inferredcites →https://doi.org/10.3390/rs14122837openalex-snapshot
Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning
inferredcites →https://doi.org/10.3390/s18082674openalex-snapshot
Machine Learning in Agriculture: A Review
inferredcites →https://doi.org/10.3390/s19214817openalex-snapshot
Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops
inferredcites →https://doi.org/10.3390/s20174915openalex-snapshot
Fast Detection of Olive Trees Affected by Xylella Fastidiosa from UAVs Using Multispectral Imaging
inferredcites →https://doi.org/10.3390/s20185249openalex-snapshot
Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields
inferredcites →https://doi.org/10.3390/s21093169openalex-snapshot
Identification of Cotton Leaf Lesions Using Deep Learning Techniques
inferredcites →https://doi.org/10.3390/s21113758openalex-snapshot
Machine Learning in Agriculture: A Comprehensive Updated Review
inferredcites →https://doi.org/10.3390/s21113813openalex-snapshot
Orchard Mapping with Deep Learning Semantic Segmentation
inferredcites →https://doi.org/10.3390/s21196657openalex-snapshot
Yield Estimation and Visualization Solution for Precision Agriculture
inferredcites →https://doi.org/10.3390/su12219138openalex-snapshot
Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture
inferredcites →https://doi.org/10.37936/ecti-cit.2022164.246857openalex-snapshot
Classification of Weeds of Paddy Fields using Deep Learning
inferredcites →https://doi.org/10.3844/jcssp.2021.284.295openalex-snapshot
Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition
inferredcites →https://doi.org/10.3897/jucs.80733openalex-snapshot
Water stress classification using Convolutional Deep Neural Networks
inferredcites →https://doi.org/10.3897/jucs.94133openalex-snapshot
Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture
inferredcites →https://doi.org/10.3906/elk-1809-181openalex-snapshot
Plant disease and pest detection using deep learning-based features
inferredcites →https://doi.org/10.3906/tar-2010-100openalex-snapshot
Apple leaf disease detection and classification based on transfer learning
inferredcites →https://doi.org/10.3991/ijoe.v17i05.21833openalex-snapshot
Accuracy and Efficiency Comparison of Object Detection Open-Source Models
inferredcites →https://doi.org/10.4025/actascitechnol.v43i1.55708openalex-snapshot
Tensor based statistical segmentation of green vegetation canopy images
inferredcites →https://doi.org/10.4081/jae.2022.1366openalex-snapshot
Monitoring mini-tomatoes growth: A non-destructive machine vision-based alternative
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.46338/ijetae0222_07openalex-snapshot
Comparative Performance Analysis of Real-Time Methods for Cassava Phytoplasma Disease (CPD) Detection based on Deep Learning Neural Networks
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.