Recent Developments and Future Prospects in the Integration of Machine Learning in Mechanised Systems for Autonomous Spraying: A Brief Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F25%3A00141868" target="_blank" >RIV/00216224:14310/25:00141868 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2624-7402/7/5/142" target="_blank" >https://www.mdpi.com/2624-7402/7/5/142</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/agriengineering7050142" target="_blank" >10.3390/agriengineering7050142</a>
Alternative languages
Result language
angličtina
Original language name
Recent Developments and Future Prospects in the Integration of Machine Learning in Mechanised Systems for Autonomous Spraying: A Brief Review
Original language description
The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10500 - Earth and related environmental sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
AgriEngineering
ISSN
2624-7402
e-ISSN
2624-7402
Volume of the periodical
7
Issue of the periodical within the volume
5
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
1-23
UT code for WoS article
001495837700001
EID of the result in the Scopus database
2-s2.0-105006542190