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A Hybrid Machine Learning and Explainable Artificial Intelligence Approach for Sustainable Crop Recommendation in Precision Agriculture

https://doi.org/10.2139/ssrn.5404609
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28/28 checkable references clean · checked 2026-09-15

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

23 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 28 checked references that resolve
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The Importance of Agriculture in the Economy: Impacts from <scp>COVID</scp>‐19
resolves10.1038/s41893-020-0510-0
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resolves10.1016/B978-0-323-91722-3.00006-3
Challenges and opportunities to sustainable crop production
resolves10.1016/j.engappai.2023.105899
Smart farming using artificial intelligence: A review
resolves10.1109/ACCESS.2020.3048415
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
resolves10.1016/j.ailsci.2021.100010
Machine learning in agriculture domain: A state-of-art survey
resolves10.1023/B:PRAG.0000040806.39604.aa
Precision Agriculture and Sustainability
resolves10.1016/j.biosystemseng.2012.08.009
Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
resolves10.1016/j.agsy.2017.01.023
Big Data in Smart Farming – A review
resolves10.1007/s11042-023-16105-2
Machine learning in agriculture: a review of crop management applications
resolves10.1016/j.inpa.2020.12.003
Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features
resolves10.1016/j.atech.2022.100081
Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning
resolves10.32628/CSEIT2173129
Crop Recommendation System using Machine Learning
resolves10.1109/ICSSIT48917.2020.9214190
Crop Prediction using Machine Learning
resolves10.1109/CSITSS.2018.8768790
Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique
resolves10.3390/app13169288
Crop Prediction Model Using Machine Learning Algorithms
resolves10.3390/agriculture14081256
A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
resolves10.1038/s41598-023-42356-y
Machine learning-based optimal crop selection system in smart agriculture
resolves10.3390/agriculture13112141
Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices
resolves10.3389/fpls.2023.1234555
Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation
resolves10.3389/fpls.2024.1451607
Next-gen agriculture: integrating AI and XAI for precision crop yield predictions
resolves10.1007/s00521-023-09391-2
Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making
resolves10.1111/coin.12629
XAI‐driven model for crop recommender system for use in precision agriculture
resolves10.1016/j.jclinepi.2020.03.002
Logistic regression was as good as machine learning for predicting major chronic diseases
resolves10.1007/s12559-021-09848-3
COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier
resolves10.1007/978-0-387-88615-2_4
k-Nearest Neighbor Classification
resolves10.1109/21.97458
A survey of decision tree classifier methodology
resolves10.1080/01431160412331269698
Random forest classifier for remote sensing classification
The 23 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedFarmer's perception and factors determining the adaptation decisions to cope with climate change: An evidence from rural India
no DOI — not checkedIntelligent crop recommendation system using machine learning
no DOI — not checkedExplainable artificial intelligence: a comprehensive review
no DOI — not checkedPermutation feature importance-based fusion techniques for diabetes prediction
no DOI — not checkedref13
no DOI — not checkedIntelligent crop recommendation system using machine learning
no DOI — not checkedAn effective crop recommendation method using machine learning techniques
no DOI — not checkedFarm-Level Smart Crop Recommendation Framework Using Machine Learning
no DOI — not checkedref26
no DOI — not checkedCrop yield prediction using machine learning models: Case of Irish potato and maize
no DOI — not checkedAn artificial intelligence-based crop recommendation system using machine learning
no DOI — not checkedPrecision agriculture for small to medium size farmers-an IoT approach
no DOI — not checkedIntegrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security
no DOI — not checkedref37
no DOI — not checkedref38
no DOI — not checkedref40
no DOI — not checkedref41
no DOI — not checkedref42
no DOI — not checkedGaussian Na�ve Bayes algorithm: a reliable technique involved in the assortment of the segregation in cancer
no DOI — not checkedref49
no DOI — not checkedAI-Farm: A crop recommendation system
no DOI — not checkedref51
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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