MH-Weed16:An Indian Multiclass Annotated Weed Dataset for Computer Vision Tasks
Description
Weeds are invasive plants that compete with crops for vital nutrients and often attract pests, significantly impacting agricultural productivity. They account for approximately 45% of the annual productivity loss in farming. Manual weeding methods, while effective, are labor-intensive and financially cumbersome, particularly for smallholder farmers. On the other hand, excessive reliance on chemical herbicides has led to herbicide resistance in several weed species, causing additional challenges in weed management.Emerging technologies, particularly artificial intelligence (AI) and computer vision, is revolutionizing the agricultural sector by automating labor-intensive tasks. Computer vision, in particular, uses advanced computational models to analyze visual cues from images, enabling the development of autonomous systems capable of performing tasks that often exceed the capabilities of the human visual system. In the context of robotic weeding, computer vision facilitates the precise identification of crops and weeds, allowing targeted herbicide application on weeds. To integrate these technologies high-quality datasets are critical component for development of accurate and robust models. So to address this need, the comprehensive MH-Weed16 Image Dataset is created from soybean fields of Maharashtra region located in India. Data acquisition was conducted from July 2023 to November 2023, ensuring a diverse natural field conditions. The dataset comprises total 18395 images of 16 weed species annotated under the guidance of subject-matter experts from agriculture universities. This dataset aims to serve as a foundational resource for training and evaluating machine learning and deep learning models. It will facilitate computer vision tasks of object detection and classification. Additionally, the dataset includes a total of 7,577 representative samples of crops along with weeds, categorized into three folders, with 6,656 weed samples annotated using bounding boxes. The images of crop with weed are captured from a top-down view to ensure accurate weed area estimation based on the bounding box areas. The MH-Weed16 dataset represents a significant step forward in the integration of technology for weed management strategies contributing towards sustainable agriculture practices