A Critical Analysis Toward Sustainable Farming: The Role of AI in Weed Identification and Management

An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. The system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. The weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. These images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. This paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. Through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep Learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. The ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.