ReviewJournal of imaging2019
Machine Vision Systems in Precision Agriculture for Crop Farming.
Review in Journal of imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Maize Detection and Row Extraction Using Maize-YOLO and IPM-Clustering Method for Autonomous Agricultural Navigation.Sensors (Basel, Switzerland) · 2026Article
- Leaf-DETR: Progressive adaptive network with lower matching cost for dense leaves detection.Plant phenomics (Washington, D.C.) · 2026Article
- Beech leaf disease symptom detection using deep learning and computer vision tools.Journal of nematology · 2026Article
- Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- Enhancing Instance Segmentation in Agriculture: An Optimized YOLOv8 Solution.Sensors (Basel, Switzerland) · 2025Article
- Hybrid deep learning model for density and growth rate estimation on weed image dataset.Scientific reports · 2025Article
- Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchards.Data in brief · 2025Article
- Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.PloS one · 2025Article
- Kidney bean detection in complex agricultural scenarios based on the kidneyB-YOLO model.Frontiers in plant science · 2025Article
- Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing.Plants (Basel, Switzerland) · 2024Article
- Smart Sensors and Smart Data for Precision Agriculture: A Review.Sensors (Basel, Switzerland) · 2024Review
- Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement.Sensors (Basel, Switzerland) · 2024Article
- Intelligent robotics harvesting system process for fruits grasping prediction.Scientific reports · 2024Article
- Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.Sensors (Basel, Switzerland) · 2024Article
- Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.Sensors (Basel, Switzerland) · 2023Article
- VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.Scientific data · 2023Article
- Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.