Eyes and Algorithms on the Field: A Novel Review of Machine Learning and Computer Vision in Precision Agriculture
Keywords:
Automation, Farming, Machine, Precision, Prediction, VisionAbstract
Precision agriculture integrates advanced sensing and analytical technologies to optimize crop management, reduce resource waste, and improve yield. This review examines the emerging role of Machine Learning (ML) and Computer Vision (CV) in transforming agricultural practices. By leveraging image-based data from drones, satellites, and ground sensors, CV enables automated crop monitoring, weed detection, disease diagnosis, and yield estimation. ML algorithms process this data to generate predictive insights, enabling site-specific interventions and efficient decision-making. The paper highlights state-of-the-art approaches, including deep learning models for plant health classification and object detection frameworks for precision spraying. Challenges such as data-set variability, environmental noise, and computational constraints are discussed alongside potential solutions. Furthermore, future trends point toward edge AI, multi-modal sensing, and climate-resilient crop analytics. This synthesis underscores the trans-formative potential of ML and CV, paving the way for sustainable, data-driven farming in the face of global food security challenges.
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