The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Machine learning in agriculture domain: A state-of-art survey
Open accessAbstract
Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfills humans’ basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like India. Agriculture contributes 15.4% in the GDP of India. Agriculture activities are broadly categorized into three major areas: pre-harvesting, harvesting and post harvesting. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive survey of latest machine learning application in agriculture to alleviate the problems in the three areas of pre-harvesting, harvesting and post-harvesting. Application of machine learning in agriculture allows more efficient and precise farming with less human manpower with high quality production.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
On hyperparameter optimization of machine learning algorithms: Theory and practice
Yang, L., & Shami, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.061 · Cited by 3,232
AutoML: A survey of the state-of-the-art
He, X., Zhao, K., & Chu, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106622 · Cited by 1,691
Savary, S., Ficke, A., Aubertot, J.-N., & Hollier, C. (2012) · Food Security · doi:10.1007/s12571-012-0200-5 · Cited by 1,208
Machine Learning Applications for Precision Agriculture: A Comprehensive Review
Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021) · IEEE Access · doi:10.1109/access.2020.3048415 · Cited by 1,102
Plant Disease Detection and Classification by Deep Learning
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019) · Plants · doi:10.3390/plants8110468 · Cited by 886
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
Liu, B., Zhang, Y., He, D., & Li, Y. (2017) · Symmetry · doi:10.3390/sym10010011 · Cited by 797
Plant disease identification from individual lesions and spots using deep learning
Arnal Barbedo, J. G. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.02.002 · Cited by 783
Performance of deep learning vs machine learning in plant leaf disease detection
Sujatha, R., Chatterjee, J. M., Jhanjhi, N., & Brohi, S. N. (2021) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103615 · Cited by 653
Fruits and vegetables quality evaluation using computer vision: A review
Bhargava, A., & Bansal, A. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.06.002 · Cited by 631
Cited by
Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
A comparative study of deep learning and internet of things for precision agriculture
Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K. S. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106034 · Cited by 232
The Dismal Harvest: The Uneven Landscapes of AI in Agriculture
Cieslik, K. (2026) · Transactions of the Institute of British Geographers · doi:10.1111/tran.70043 · Cited by 4
Metadata
- Authors
- Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D.
- Venue
- Artificial Intelligence in the Life Sciences
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 1
- Pages
- 100010
- ISSN
- 2667-3185 (ISSN-L)
- OpenAlex
- W3201835768
- Cited by
- 367
- References
- 45
- FWCI
- 31.12
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Machine learning in agriculture domain: A state-of-art survey. https://app.cochat.ai/papers/10.1016/j.ailsci.2021.100010
- The paper
- Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences. https://doi.org/10.1016/j.ailsci.2021.100010
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
