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

shorewala, Shantam and Ashfaque, Armaan and Sidharth, R and Verma, Ujwal (2021) Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning. IEEE Access, 9. pp. 27981-27986. ISSN 2169-3536

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Abstract

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

Item Type: Article
Uncontrolled Keywords: Artificial intelligence, artificial neural networks, computer vision, convolutional neural networks, deep learning, crops, weeds, machine learning, neural networks, precision agriculture, ResNet, segmentation, semi-supervised learning, unsupervised learning.
Subjects: Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 23 Apr 2021 05:59
Last Modified: 23 Apr 2021 05:59
URI: http://eprints.manipal.edu/id/eprint/156647

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