Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 25, 2024Deleted Journal

Prediction and Counting of Soybean Seed Pod Image Based on Seed Counter Convolution Neural Network

View Full Paper
Ask AI
Bookmark
Share

Authors

VPV. K. Patil

Discussion

Loading...

Member takes

Overview

Deep learning evaluation demonstrates up to 97% seed counting accuracy in soybean pod images, highlighting efficient phenotypic analysis for crop breeding.

Key Points

  • A multi-column seed counter convolution neural network achieves up to 97% prediction probability for soybean pod counts, overcoming manual counting bottlenecks.
  • Assessment using the Soybean-case dataset evaluates 500 pod photos alongside 100 augmented images across distortion, high-density, and low-pixel test sets with a loss function.
  • Supports enhanced crop breeding by accelerating phenotypic indicator measurements such as hundred-grain weight, helping resolve soybean supply and demand mismatches.

Cite This Study

V. K. Patil (2024) studied this question.

synapsesocial.com/papers/68e6dc0eb6db643587657d07https://doi.org/10.52783/jes.5831
View Full Paper
Ask AI
Bookmark
Share

Also Consider

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

  1. 1Accurate and fast implementation of soybean pod counting and localization from high-resolution image2024 · 21 citations
  2. 2SPCN: An Innovative Soybean Pod Counting Network Based on HDC Strategy and Attention Mechanism2024 · 7 citations
  3. 3SoybeanNet: A Lightweight Neural Network for Soybean Pod Detection and Quantification2025
  4. 4SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment2026
  5. 5Soybean crop yield estimation using artificial intelligence techniques2024