Development And Simulation Of Ios App To Detect Cotton Plant Diseases Using Machine Learning Techniques

Kumar, Sandeep; ratan, Rajiv; Desai, Jagan

doi:10.2139/ssrn.4007166

Cited by 2 publications

(1 citation statement)

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“…App development uses Apple's core ml framework. The app is designed on VIPER iOS app architecture, which has numerous levels [34].…”

Section: Image Processingmentioning

confidence: 99%

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Hassan,

Malik,

Irtaza

et al. 2022

VFAST trans. softw. eng.

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Among all the agrician products, cotton is known as “Ready Cash Crop” and it plays the significant role in the stability of the economy of a country. Therefore, it is extremely important to monitor the cotton crop from the numerous diseases. Unfortunately, sometimes human eyes not be able to analyze these diseases at earlier stage and that will affect not only the quality and also the quantity of the cotton crops. To address this early monitoring issue we proposed an interactive framework based on target feature extraction and deep learning model for cotton leaf screening to deal with these well-known dangerous diseases; Grey Mildew, Cercospora, Bacterial Blight and Alternaria. In this study we chosen our own collected dataset that contains 522 images of cotton leaves that were collected from the field (Cotton agricultural areas near the Multan city). The performance evaluation matric indicates the algorithm secure; 85.42% overall accuracy, 0.8542 precision, 0.8542 recall, 0.854 F1 score and 0.817 kappa coefficient indicates the generalization and acceptability of the model. The proposed framework not only assists the agronomist but also the farmer because of early identification of diseases from cotton crop and to avoid from the massive loss. It make better decisions for cotton crop management and contributes in the sustainability of the economy.

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“…App development uses Apple's core ml framework. The app is designed on VIPER iOS app architecture, which has numerous levels [34].…”

Section: Image Processingmentioning

confidence: 99%

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Hassan,

Malik,

Irtaza

et al. 2022

VFAST trans. softw. eng.

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An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

Sahu

Pandey²

2023

Expert Systems with Applications

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Development And Simulation Of Ios App To Detect Cotton Plant Diseases Using Machine Learning Techniques

Cited by 2 publications

References 1 publication

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant

An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

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