ReviewSensors (Basel, Switzerland)2021
Machine Learning in Agriculture: A Comprehensive Updated Review.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 2 of them syntheses 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
85 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Human-Robot Interaction in Agriculture: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Influence of blue, yellow, and red light spectra on growth performance and physiological development of Japanese Quail (Coturnix japonica) under a floor-based rearing system.Poultry science · 2026Article
- Machine Learning Techniques to Predict Fetal Nutritional Status.Healthcare informatics research · 2026Article
- Modeling the Adaptation of Dairy Cows to Automatic Milking Systems Using Statistical Methods and Machine Learning: Development of the Robotic Adaptability Index.Animals : an open access journal from MDPI · 2026Article
- Tomato Ripeness Detection and Localization Based on the Intelligent Inspection Robot Platform.Sensors (Basel, Switzerland) · 2026Article
- A novel hybrid NSGA-III and machine learning framework for modeling wheat yield variability using climatic, edaphic, and nutritional drivers.Scientific reports · 2026Article
- Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost.Scientific reports · 2026Article
- SCEA-YOLO: A General-Purpose Maturity Grading Model of Multi-Crop Greenhouse Robots.Plants (Basel, Switzerland) · 2026Article
- Digital and Green Technological Drivers of Transformation in the Agri-Food Sector.Foods (Basel, Switzerland) · 2026Review
- AgroNova: An Autonomous IoT Platform for Greenhouse Climate Control.Sensors (Basel, Switzerland) · 2026Article
- Phenotypic classification of opium poppy genotypes (Papaver somniferum L.) based on morpho-phenological traits.Scientific reports · 2026Article
- Generalizable long short-term memory models for beef cattle DMI under grazing.Journal of animal science · 2026Article
- Artificial intelligence-based dairy cattle behavior recognition for estrus detection via ensemble fusion of two camera views.PloS one · 2026Article
- New challenges in horticultural IPM: artificial intelligence can enhance the use of biocontrol agents.Open research Europe · 2026Review
- Green Nanoparticles in Precision Agriculture: A Critical Review of Smart Crop Management, Nano-Enabled Delivery Systems, and Sustainable Farming.Nanotechnology, science and applications · 2026Review
- A Novel Method for Estimating the Body Weight and Size of Sows Using 3D Point Cloud.Animals : an open access journal from MDPI · 2025Article
- Perspectives on morphology, physiology, genetic polymorphism and machine learning in cucumber grafting under zinc toxicity.BMC plant biology · 2025Article
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges.Sensors (Basel, Switzerland) · 2025Review
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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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.