CNN Based Disease Detection in Apple Leaf via Transfer Learning

Sahu, K M; Saraswat, Tanya; Singhal, Abhishek; Langer, Gabriele

doi:10.1109/cictn57981.2023.10141259

Cited by 5 publications

(1 citation statement)

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“…The data was divided into training and validation sets using an 80:20 ratio in this study. K. Sahu et al [6] argue that the extensive and consistent use of pesticides is supported by the significant economic impact of plant diseases on field crops. Plant diseases in some areas may hinder the cultivation of food plants or lower their production by attacking and perhaps killing them before harvest.…”

Section: IImentioning

confidence: 99%

Extraction of Heterogeneous Features from Various Fruits and Classification of diseases using Deep Learning Techniques

Mukesh Shrimali

2024

jes

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The success of the farming business and the economy as a whole is directly tied to each individual farmer's output. Micro-level illnesses that emerge during fruit development may have a significant influence on output. There is an overwhelming variety of viruses, necessitating improvements in image processing and other informational resources before effective treatments can be proposed. Unparalleled approaches to segmenting, extracting characteristics, and classifying in order to identify overt conditions constitute a substantial portion of the research in this domain. However, in the event of larger and more in-depth datasets, machine learning and artificial intelligence technologies like deep learning and the like are necessary to discover illnesses that would otherwise be invisible to the human eye. Machine designers are not always sure what kinds of illnesses can be identified by mixing algorithms since there are so many possible combinations. In this paper we proposed a fruit disease detection and classification using deep convolutional neural network. The VGG-16 and VGG-19 both deep learning frameworks are utilized for detection of heterogeneous fruits. The both deep learning frameworks are archives 96.10% and 98.10% average accuracy on heterogeneous fruit dataset. In overall analysis the VGG-19 obtains higher accuracy with 200 epoch size which is higher the VGG-16 and other conventional machine learning and deep learning frameworks.

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Section: IImentioning

confidence: 99%