Semantic Segmentation of Plant Structures with Deep Learning and Channel-wise Attention Mechanism

• Autorzy: Surehli Mukund Kumar , Aggarwal Naveen , Joshi Garima , Nayyar Harsh

Identyfikatory

DOI

10.26636/jtit.2025.1.1853

• Języki publikacji: EN

Abstrakty

EN

Semantic segmentation of plant images is crucial for various agricultural applications and creates the need to develop more demanding models that are capable of handling images in a diverse range of conditions. This paper introduces an extended DeepLabV3+ model with a channel-wise attention mechanism, designed to provide precise semantic segmentation while emphasizing crucial features. It leverages semantic information with global context and is capable of handling object scale variations within the image. The proposed approach aims to provide a well generalized model that may be adapted to various field conditions by training and tests performed on multiple datasets, including Eschikon wheat segmentation (EWS), humans in the loop (HIL), computer vision problems in plant phenotyping (CVPPP), and a custom “botanic mixed set” dataset. Incorporating an ensemble training paradigm, the proposed architecture achieved an intersection over union (IoU) score of 0.846, 0.665 and 0.975 onEWS, HIL plant segmentation, and CVPPP datasets, respectively. The trained model exhibited robustness to variations in lighting, backgrounds, and subject angles, showcasing its adaptability to real-world applications.

Słowa kluczowe

EN

channel-wise attention; computer vision

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2025
• Tom: nr 1
• Strony: 56--66
• Opis fizyczny: Bibliogr. 28 poz., rys., wykr.

Twórcy

autor

Surehli Mukund Kumar

• VIT-Bhopal University, Madhya Pradesh, India

• https://orcid.org/0009-0004-6111-8149

autor

Aggarwal Naveen

• Panjab University, Chandigarh, India

autor

Joshi Garima

• Panjab University, Chandigarh, India

autor

Nayyar Harsh

• Panjab University, Chandigarh, India

• https://orcid.org/0000-0001-6005-4391

Bibliografia

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