Precision Farming: Advanced Methods for Weed Identification

Identifying weed is an important problem for a sophisticated observation system since it gives data for semantic interpretation of acquired details. Given its potential to improve the development of wellness frameworks, its extension into automotive applications is clear. For a sophisticated monitoring observation system, identifying weeds is an important task since it gives information for the semantic interpretation of the recorded material. Its extension to automotive applications makes sense given its ability to further the creation of wellness frameworks. However, natural existence weeds may occur if the AWAS systems notice weeds in later stages but fail to activate the alarm to the farmer in a timely manner. Weeds allowing the crops to lose sufficient nutrients and affects its regular growth. This paper explores recent advancements in visual weed detection with deep learning algorithms. The presentation will provide an overview of deep learning basics for weed detection, followed by a discussion of current advancements in the field. This review paper investigates the evolution of weed detecting techniques. It presents the findings of study on numerous areas, such as the identification of different species of weeds and computational imaging technologies.