Precision Agriculture: Integrating Sensors for Weed Detection using Machine Learning in Agriculture Fields

Weed control is a critical challenge in agriculture, impacting crop yields and necessitating various management strategies, from manual to chemical methods. This paper explores the integration of advanced sensor technologies with machine learning for weed detection and management. Our study discusses the application of multiple sensors RGB, multispectral, hyperspectral, thermal, LiDAR, fluorescence, and ultrasonic each providing unique advantages in detecting and differentiating weeds from crops based on spectral, thermal, and structural characteristics. We delve into the capabilities of each sensor type, underlining their individual and combined utility in addressing the complexities of agricultural environments, such as varying lighting conditions, soil types, and crop stages. This review highlights the potential of ML algorithms to refine the data processing and enhance the accuracy of weed identification systems. Our findings indicate that while these technologies offer significant improvements in detecting and managing weeds, challenges remain in their adoption, particularly among small-scale farmers due to system complexity and cost.