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CHAPTER-4
Applications of Artificial Intelligence and Robotics in Plant Sciences: From
Precision Monitoring to Data-Driven Decision Making
Muhammad Tayyab Mateen1
 ,
Liaba Nasir2
,
Waqas Mushtaq3
,
Mariam Ilyas2
Ali
Muaviah4
,
Ahsan Tanvir4
Ayesha
Safdar2
Abdal
Ali5
,
and Amjad Hameed*2
1
Department of Agronomy,
University of Sargodha, Sargodha, Pakistan
2
Nuclear Institute for Agriculture
and Biology College, Pakistan Institute of Engineering and Applied Sciences
(NIAB-C, PIEAS), Faisalabad, Pakistan
3
Jiangxi Agricultural University,
Key Laboratory of Crop Physiology, Ecology, and Genetic Breeding, Ministry
of Education College of Agronomy, 1101 Fang Zhimin Avenue, Economic and
technological Development Zone, Nanchang 330045, China
4
Department of Botany, Government College University Faisalabad (GCUF),
Faisalabad, Pakistan
5
Department of plant
and soil sciences, University of Kentucky, United States of America
DOI:
https://doi.org/10.65970/bk.ips.2025.07
Correspondence:
amjad46pk@yahoo.com
Peer Reviewed Open Access Chapter
Published in Book: Innovations in Plant Sciences, 1st
edition
Summary
Rapid advancements. The fast development of artificial intelligence,
robotics and digital sensing technologies has transformed the current day
plant sciences as it allowed a transition to be no longer based on awareness
processes to massively automated and data-intensive systems. The integration
is explored in this chapter of remote sensing with AI, machine learning,
phenotyping, robotics-assisted farming, and evidence-based decision models
throughout the agricultural systems. Emphasizing the origin of multimodal
data streams of satellites, UAVs, IoT networks, and proximal sensors are
fused algorithmically, providing continuous plant performance, early biotic
and abiotic stress detection, and predictive yield modeling. The chapter
also evaluates the introduction of self-directed field robots in planting,
irrigation, nutrient delivery, weeding and harvesting, which will emphasize
their contribution to bottlenecks of labor decreased and improving
operational precision. Also, the growing dependency on big data analytics
and decision support platforms is discussed as one of the key motivators of
efficient utilization of resources and climate-adaptive crop management.
Artificial intelligence in agriculture has ethical, economic and regulatory
consequences, as discussed to put in perspective the shift to automated,
intelligent farming ecosystems. In general, the chapter is a prospective
overview of the social transformation of AI and robotics in plant sciences
and paves the further innovations of sustainability, efficiency, and
resilience of crop production systems.
Keywords: Artificial Intelligence in Plant Sciences,
AI in Agriculture, Agricultural Robotics, Precision Agriculture, Smart
Farming, Digital Agriculture, Machine Learning in Agriculture, Deep Learning
for Crop Monitoring, Computer Vision in Agriculture, Data-Driven Agriculture
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