Model for the Identification of Diseases in the Banana Plant Using a Convolutional Neural Network

S., Fredy H. Martínez; G., Edwar Jacinto; S., Fernando Martínez

doi:10.37624/ijert/13.10.2020.2668-2673

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Chaitanya et al [22] designed a 3-layer CNN to detect four types of diseases in banana leaves. The model was trained with a 1200 image dataset and has achieved an A low-cost embedded system was designed and trained using DenseNet CNN model by Fredy et al [23] to detect diseases in banana leaves. The embedded system was able to categorize Bacterial Wilt and Black Sigatoka diseases with an accuracy of 92%.…”

Section: Identification and Prediction Of Diseases In Banana Plantsmentioning

confidence: 99%

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Sunitha,

Uma,

Geetha Kiran

et al. 2024

J. Umm Al-Qura Univ. Eng.Archit.

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Banana plants require a precise balance of 16 essential nutrients to flourish, with micronutrients playing a pivotal role in smaller proportions than macronutrients. Insufficient micronutrient provision in Banana plants can impede growth, hinder flowering, and diminish fruit production. Precise identification of deficiencies is imperative for farmers aiming to cultivate nutrient-dense crops and enhance their yields. Micronutrients such as Boron contribute to hormone regulation, while Iron facilitates enzyme function, DNA synthesis, and overall metabolic processes in plants. Observable manifestations of Boron and Iron deficiencies on banana leaves are crucial indicators necessitating intervention. The present work proposes a novel deep learning methodology, employing a CNN model with Skip Connections (CNNSC), to detect Boron and Iron deficiencies in banana foliage. The CNNSC model, featuring thirteen layers, surpasses established architectures like VGG16, DenseNet, and Inception V3. Training the model on a specialized curated dataset comprising 11,000 nutrient-deficient images, with a split of 70% for training and 30% for testing, has yielded promising outcomes. Key performance metrics, encompassing accuracy, loss, precision, F1-score, recall, and the confusion matrix underscore the efficacy of the model, achieving an impressive accuracy rate of approximately 95%.

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Section: Identification and Prediction Of Diseases In Banana Plantsmentioning

confidence: 99%

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Sunitha,

Uma,

Geetha Kiran

et al. 2024

J. Umm Al-Qura Univ. Eng.Archit.

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“…Many researchers work on noise type classification. Research by Hiremath [10] classifies noise using a pre-trained neural network using transfer learning. They have considered two types of noise; Electronic noise and Impulse noise.…”

Section: Related Workmentioning

confidence: 99%

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

Al Mudhafar,

El Abbadi

2023

AJEST

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In the last decade, the number of digital images has increased dramatically. Noise is unwanted particles or signals contaminating the image during the captured image and transmission. Image noise reduces the image quality and increases the processing failure ratio. It is highly recommended to remove the noise, and before removing the noise, we have to know the type of noise, which highly assists in suggesting the proper de-noise algorithm. This study introduces a method to effectively detect and recognize image noise of various types (Gaussian, lognormal, Rayleigh, Salt & Pepper, and Speckle). The proposed model consists of two stages: the first stage is detecting the noise in an image using Convolutional Neural Network. The second stage classifies the noisy images into one of five types of noise using a new method based on a combination of deep wavelet machine learning classifiers, we select five machine learning classifiers (support vector machine, decision tree, random forest, logistic regression, and K-nearest neighbor) to choose the more efficient classifier ultimately. The combination of wavelet with machine learning, specifically SVM, can highly enhance the results, where the accuracy was (91.30 %) through many experiments conducted to build a sturdy classification model.

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Model for the Identification of Diseases in the Banana Plant Using a Convolutional Neural Network

Cited by 2 publications

References 13 publications

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

A Convolution Neural Network with Skip Connections (CNNSC) approach for detecting micronutrients boron and iron deficiency in banana leaves

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

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