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September 10, 2025INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Detection and Segmentation of Crops and Weeds Using Deep Learning Techniques

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Authors

PSP. SoumyaRRR Nagarjuna ReddyDSDr.K.L.S. Soujanya

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Overview

Analysis demonstrates enhanced crop identification and weed segmentation using ResNet50 and YOLOv7, suggesting improvements for precision agriculture.

Key Points

  • Combining ResNet50 and YOLOv7 significantly improves crop and weed detection accuracy, enhancing environmental sustainability in agriculture.
  • The model achieved precise delineation of weeds and crops in images, utilizing deep learning for efficient identification and segmentation.
  • ResNet50's architecture enables detailed classification of crops and weeds, while YOLOv7 maps their exact locations in real time.
  • This innovative approach addresses critical agricultural challenges, aiming for improved yield and reduced herbicide use.

Cite This Study

Soumya et al. (2025) studied this question.

synapsesocial.com/papers/68c1afc654b1d3bfb60e79f4https://doi.org/10.55041/ijsrem51564
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Also Consider

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  4. 4Deep Learning Approach: Precision Agriculture Advancements Through Accurate Segmentation of Crop and Weed Density2024 · 1 citations
  5. 5Research on Weed and Crop Identification System Using Pixel-Wise Segmentation2024 · 2 citations