on to a particular problem. have a wide definition [26]. Fig 3: Combining an ensemble of classifiers for reducing classification error and/or model selection. 4.Proposed Work 4.1 Dataset: In the proposed study, 900 photographs are utilized to trainthe model, and 300 photos are used for validation. The dataset may be refered at: https://www.kaggle.com/abdallahalidev/plantvillagedataset it was collected using the Kaggle platform. The datasetcontains images from categories such as Healthy, LateBlight, and Early Blight.
Open resource ↗kaggle.com/abdallahalidev/plantvillagedataset · pdf-raw-page:4 lines:1-74Unverified paper record
Enhanced Disease Detection for Potato Crop Using CNN with Transfer Learning
International Journal on Recent and Innovation Trends in Computing and Communication · 5 Nov 2023 · 10.17762/ijritcc.v11i9.9372
Abstract
As the fourth most popular basic food in the world,potatoes are widely available. In addition, the worldwidemarket is causing the demand to rise daily. Diseases likeearly and late blight have a significant impact on the quantity and quality of potatoes. Determining which potato leaves are afflicted with a certain illness becomes more challenging when interpreting these diseasesmanually. Thankfully, it is possible to identify potato leafdiseases by examining the leaf conditions. This proposedstudy presents a technique that employs deep learning toidentify the two types of diseases and generates an accurate classifier using heavy designs for convolutionalneural networks, such as GoogleNet, Resnet15, VGG16,and Xception. We achieved 97% accuracy in the first 40 CNN epochs, demonstrating the practicality of the deep neural network approach.
Plant phenotyping relevance
ジャガイモ葉の病徴を画像からCNNで分類する手法が研究の中心であり、植物の病害状態を直接推定している。
abstractThis proposedstudy presents a technique that employs deep learning toidentify the two types of diseases and generates an accurate classifier using heavy designs for convolutionalneural networks
abstractWe achieved 97% accuracy in the first 40 CNN epochs, demonstrating the practicality of the deep neural network approach.
Code and data availability
The paper's potato disease classification experiments are built directly on a public Kaggle PlantVillage image dataset (900 training / 300 validation images of Healthy, LateBlight, EarlyBlight potato leaves), which the authors explicitly reference with a public URL. No author code, models, or other paper-specific phenp
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