DEEP LEARNING BASED WEED DETECTION IN AGRICULTURE

Abstract: In the context of agriculture, a weed is any plant that grows where it is not wanted and competes with cultivated plants for nutrients, water, and sunlight. Weeds can pose significant challenges to crop cultivation by reducing yields, interfering with harvest operations, and increasing production costs. They can also harbor pests and diseases, further impacting crop health and productivity. Controlling weeds is an essential aspect of modern agriculture, and various strategies, including mechanical cultivation, chemical herbicides, crop rotation, and mulching, are employed to manage weed populations and minimize their impact on crop production.

Traditional methods of weed identification in agriculture, relying on visual inspection by farmers, are time-consuming and prone to errors due to the vast diversity of weed species. This project proposes an approach to weed identification utilizing deep learning and image processing techniques.