Weed Detection using Image Processing

Abstract

In today’s farming world, it’s crucial to grow crops efficiently and without harming the environment. One big problem is dealing with weeds that can hurt crops and lead to more pesticide use. This research explores a new way to solve this problem using computers and pictures. Traditionally, people used expensive and not-so-environmentally-friendly methods to control weeds. But now, we can use technology like high-quality cameras on drones and satellites to take pictures of fields. Then, we use smart computer programs to figure out which plants are crops and which ones are weeds. This helps us target the weeds more precisely without harming the crops. A multiclass setup is used to detect and categorize corn and soybeans in images. Diverse datasets with annotations are collected. Convolutional Neural Networks (CNNs) extract features from images, and a neural network model, like UNet, classifies them as “corn”, “soybean” or “neither”. The model is pre-trained on large datasets and fine-tuned for agriculture. Training and validation sets assess its performance. Real-time deployment aids farmers, and postprocessing refines results. Periodic retraining adapts to changing conditions. This approach enhances precision agriculture. By bringing together technology and farming, we’re creating a new way to fight weeds. This research aims to contribute to the conversation about making farming more precise and ecofriendly. It’s all about using technology to make farming better for the environment and more efficient. In simple terms, this research is about using computers and advanced cameras to help farmers grow crops better and control weeds more effectively while taking care of the environment.