Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

• Published in: IEEE access 2021, Vol.9, p.27971-27986
• Main Authors: Shorewala, Shantam, Ashfaque, Armaan, Sidharth, R., Verma, Ujjwal
• Format: Article
• Language: English
• Subjects: Agricultural land; Agriculture; Artificial intelligence; Artificial neural networks; Autonomous robots; Biodiversity; Chemical treatment; Color imagery; computer vision; convolutional neural networks; Crop yield; crops; Datasets; Deep learning; Density; Flowers & plants; Herbicides; Image acquisition; Image segmentation; machine learning; neural networks; Object segmentation; precision agriculture; ResNet; Robots; segmentation; Semi-supervised learning; Training; unsupervised learning; Vegetation mapping; Weeds
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3057912

Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data.