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October 9, 2025Engineering Technology & Applied Science ResearchOpen Access

An Optimized Deep Learning Approach for Early Weed Detection in Chili Crop Habitats

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Authors

BSB. R. SayiprathapRYRajesh YakkundimathLTLikewin Thomas

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Overview

This research demonstrates a deep learning model's effectiveness in classifying chili weeds, indicating its potential for automated detection in agriculture.

Key Points

  • MobileNetV2 achieved the highest classification accuracy of 96.6% for chili weed detection.
  • The study compared three CNN architectures: MobileNetV2, ResNet50, and VGG16 on a tailored dataset.
  • Conventional plant identification is resource-intensive, making automated approaches desirable in agriculture.
  • The lightweight design of MobileNetV2 allows for efficient deployment on edge devices with limited resources.

Cite This Study

Sayiprathap et al. (2025) studied this question.

synapsesocial.com/papers/68e70db790569dd607ee6507https://doi.org/10.48084/etasr.11385
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Implementation of machine learning algorithms for accurate identification and classification of weeds2026
  2. 2Weed Identification Using Deep Learning in Vegetable Farming2024
  3. 3Optimizing Crop Management: Customized CNN for Autonomous Weed Identification in Farming2024
  4. 4Effective detection of weeds in sesame crop2026
  5. 5An Image-Based Plant Weed Detector Using Machine Learning2024 · 1 citations