Use Of Convolutional Neural Networks In Detection Of Branch Dieback Disease In Pequi Trees

Key points

• This disease represents a severe threat to tree development and fruit yield, causing substantial economic and ecological losses.1
• The methodology implements computer vision techniques using Python libraries, including Scikit-learn, NumPy, TensorFlow, and Matplotlib, to process and evaluate visual data.1
• The CNN models analyze images of leaves, branches, and flower spikes to detect pathological patterns, such as alterations in texture and coloration.1
• Ultimately, this comparative analysis identifies the most effective architecture for accurately distinguishing healthy plants from diseased ones.1

Sources (1)

• [1]Use Of Convolutional Neural Networks In Detection Of Branch Dieback Disease In Pequi Trees
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 04:40 PM

  This study aims to evaluate and compare convolutional neural network (CNN) architectures, specifically AlexNet, Inception, LeNet, VGG16, and MobileNet, for diagnosing branch dieback (seca-dos-ponteiros) in pequi trees (Caryocar brasiliense).

  This disease represents a severe threat to tree development and fruit yield, causing substantial economic and ecological losses.

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