Efficient Weed Detection in Agricultural Landscapes using DeepLabV3+ and MobileNetV3

Weed detection is a crucial task in precision agriculture, significantly impacting crop yields and reducing the dependency on herbicides.Effective weed management enhances agricultural productivity by ensuring that crops receive adequate nutrients, water, and sunlight, which weeds would otherwise consume.Traditional weed control methods are labor-intensive and often rely heavily on chemical herbicides, which can have detrimental environmental effects.This paper presents a deep learning approach for weed detection, utilizing the DeepLabv3+ model with a MobileNetv3 backbone.This study underscores the potential of integrating advanced deep learning techniques into agricultural practices, paving the way for more sustainable and efficient weed management strategies.