Weed Detection Using MobileNetV2

Effective weed detection is pivotal in modern agriculture for optimizing crop yields, conserving biodiversity, and reducing reliance on herbicides. Traditional methods, such as manual inspection and chemical treatments, are labor-intensive, costly, and can negatively impact the environment. This paper presents a deep learning-based weed detection system utilizing the MobileNetV2 convolutional neural network, pre-trained on ImageNet and fine-tuned for agricultural imagery. The system classifies images into ‘weed’ or ‘not weed’ categories by extracting and analyzing visual features, enabling rapid and accurate identification of unwanted vegetation. Integrated within a user-friendly Flask web application, the solution provides real-time feedback to users and features distinct admin and user modules for streamlined management and support. Experimental results demonstrate the model’s robustness and high accuracy in distinguishing weeds from crops, underscoring the potential of deep learning to revolutionize weed management practices. This approach offers a scalable, precise, and environmentally sustainable alternative to conventional weed control, contributing to the advancement of smart farming and sustainable agriculture.