ReviewHeliyon2024
Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Maize yield prediction using machine learning: a systematic literature review.Frontiers in artificial intelligence · 2026Pooled it
- HAE-Net: A hierarchy-aware enhanced method for 3D panoptic segmentation in orchard scenes.Plant phenomics (Washington, D.C.) · 2026Article
- Phenotypic Differentiation Between Highland and Coastal Quinoa Under Cold Stress Conditions.Plant, cell & environment · 2026Article
- Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment.Foods (Basel, Switzerland) · 2026Review
- Food Production Index Forecasting for Sustainable Food Systems in Türkiye: A Machine Learning-Based Approach.Foods (Basel, Switzerland) · 2026Article
- A feature-centric predictive and sensitivity analysis framework for bio-oil yield assessment in biomass pyrolysis.Scientific reports · 2026Article
- Multi-Horizon Herd-Based Cattle Live-Weight Forecasting Using Irregular Automated Weighing Data.Animals : an open access journal from MDPI · 2026Article
- A unified RepVGG model factory with plug-in SE attention and GeM pooling for fine-grained agricultural classification.Scientific reports · 2026Article
- Digital and Green Technological Drivers of Transformation in the Agri-Food Sector.Foods (Basel, Switzerland) · 2026Review
- Explainable AI-Based Hyperspectral Classification Reveals Differences in Spectral Response over Phenological Stages.Biology · 2026Article
- Wheat yield prediction using integrated optical and radar remote sensing with machine learning across key phenological stages.Scientific reports · 2026Article
- WaveST-Yield: a novel spatio-temporal deep learning framework with frequency-domain refinement for UAV-based maize yield prediction.Frontiers in plant science · 2026Article
- Multitask ST-LSTM model based on UAV hyperspectral remote sensing for wheat yield prediction.Frontiers in plant science · 2026Article
- A review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations.Frontiers in plant science · 2026Review
- Morphometric trait analysis and machine learning-based yield modeling in wood apple (Feronia limonia L.).BMC plant biology · 2025Article
- Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.Scientific reports · 2025Article
- NDVI and Beyond: Vegetation Indices as Features for Crop Recognition and Segmentation in Hyperspectral Data.Sensors (Basel, Switzerland) · 2025Article
- Digital twin-based applications in crop monitoring.Heliyon · 2025Review
- Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features.Frontiers in plant science · 2025Article
- Editorial: Semantics and natural language processing in agriculture.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas. The review identifies a wealth of AI-powered solutions employed in crop yield prediction, emphasizing the importance of precise environmental and agricultural data. Key factors contributing to accurate estimation include temperature, rainfall, soil type, humidity, and various vegetation indices, such as NDVI, EVI, LAI, and NDWI. The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). In the realm of deep learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN) emerge as promising candidates. The findings of this study shed light on the transformative potential of advanced AI techniques in improving crop yield estimation accuracy, ultimately enhancing agricultural planning and resource management. By addressing the challenges posed by geographical diversity, crop heterogeneity, and changing environmental conditions, AI-driven models offer new avenues for sustainable agriculture in an ever-evolving world. This research paper provides valuable insights and directions for future studies, highlighting the critical role of AI in ensuring food security and sustainability in agriculture.
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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.