Design and Development of Weed Detection Mechanism in Wheat using AI

DOI

10.2991/978-94-6239-628-9_12How to use a DOI?

Keywords

Smart Agriculture; Deep Learning; SegNet; Weed Classification

Abstract

Deep learning constitutes an ongoing, modern technique for image processing and data analysis, with promising outcomes and enormous potential. As deep learning has been effectively applied in different domains, it has likewise entered the area of agriculture. Weed control is important for high quality yields as it works the same as pest for crops. It is the need of the hour to limit the effects and price of non-essential herbicides in turn to reduce ill effects on the health of living beings as well as crops, to attain the goal of sustainable agriculture. In this paper, we have performed a comparison between the algorithms of machine learning and deep learning. SVM with non-linear kernel function and Random Forest is applied to obtain the classification accuracy. We also proposed a novel weed detection mechanism that relies on deep fully convolutional neural network architecture which consists of an encoder-decoder structure followed by a pixel-wise classification layer, termed as SegNet. Alongside SegNet, another convolution network architecture, called U-Net is also applied. The proposed approach is experimentally validated on detection of Phalaris minor weed in wheat crops. The dataset contains RGB images taken during the month of December and January within the area of Punjab. The findings indicate that SegNet and U-Net provide higher accuracy and outperforms other commonly used techniques.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Eknoor Kaur
AU  - Gaurav Verma
AU  - Abhishek Kumar Saxena
AU  - Mritunjay Rai
AU  - Jatin Gaur
PY  - 2026
DA  - 2026/03/31
TI  - Design and Development of Weed Detection Mechanism in Wheat using AI
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 117
EP  - 130
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_12
DO  - 10.2991/978-94-6239-628-9_12
ID  - Kaur2026
ER  -