bability of beggars and producers in terms of searching for food. The pseudo-code of the sug- gested HBM-BSO is given here, Algorithm 1. 4.2 Description of Datasets The developed multi-disease plant leaf classification model gathered the images from standard online sources. The selected input images are obtained from the link “https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset: access date: 2022-05-02”. Here, sample images are collected from the dataset kaggle, whereas the original dataset is collected from the GitHub repo. This dataset holds nearly 87 k rgb healthy and non-healthy images of plant leaves, and it is classified into 37 varie- ties of classes. The whole dataset is s
Open resource ↗kaggle · lavaman151/plantifydr-dataset · pdf-raw-page:21 lines:1-29Unverified paper record
Adaptive Segmentation with Intelligent ResNet and LSTM–DNN for Plant Leaf Multi-disease Classification Model
Sensing and Imaging · 3 Jul 2023 · 10.1007/s11220-023-00428-3
Abstract
Abstract has not been obtained from indexed metadata or an accessible article page.
Plant phenotyping relevance
植物葉の画像を対象に、適応的セグメンテーションとResNet/LSTM-DNNによる多病害分類モデルを開発する研究であり、葉の病害状態を画像から推定する手法が中心と判断できる。
titleAdaptive Segmentation with Intelligent ResNet and LSTM–DNN for Plant Leaf Multi-disease Classification Model
Code and data availability
The paper's leaf-disease classification experiments use the publicly available PlantifyDR Kaggle dataset (~87k RGB leaf images, 37 classes) as its input image data. No author code, models, or supplementary deposits are mentioned.
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