Article: Evaluation of CNN-based computer vision recommended treatments for recognised guava disease Journal: International Journal of Embedded Systems (IJES) 2023 Vol.16 No.5/6 pp.354 - 363 Abstract: Climate change poses a particular threat to the agricultural crop production sector. The entire food industry is affected by this issue, not just the farming sector. The diagnosis of plant diseases could be improved by using deep learning strategies, according to several studies. These samples are rarely analysed for their ability to predict quality. Extreme caution is required to organise agricultural output surgically. Detecting high incidence rates in commercial production is difficult because of the unfair model's unpredictability, resulting in more difficulty in diagnosing reflex plant diseases. The proposed model is designed to identify the guava disease using convolutional neural networks (CNNs) and machine learning for classification. An autoencoder is used to divide the neural network design in the encoder and decoder. The linear support vector machine is used as a classification to analyse the outcomes of our experiments. Preliminary results from the suggested model indicate a remarkable degree of accuracy (97.5%). Inderscience Publishers - linking academia, business and industry through research

Title: Evaluation of CNN-based computer vision recommended treatments for recognised guava disease

Authors: Vishal Kumar Kanaujia; Satya Prakash Yadav; Awadhesh Kumar; Victor Hugo C. de Albuquerque; Caio dos Santos Nascimento

Addresses: Computer Science and Engineering-Data Science Department, ABES Engineering College Ghaziabad, UP, India ' Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida-201306, India; Graduate Program in Telecommunications Engineering (PPGET), Federal Institute of Education, Science, and Technology of Ceará (IFCE), Fortaleza-CE, Brazil ' Department of Computer Science and Engineering, Kamala Nehru Institute of Technology Sultanpur – Kadipur Rd, Sultanpur, Uttar Pradesh 228118, India ' Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Fortaleza/CE, Brazil ' Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Fortaleza/CE, Brazil

Abstract: Climate change poses a particular threat to the agricultural crop production sector. The entire food industry is affected by this issue, not just the farming sector. The diagnosis of plant diseases could be improved by using deep learning strategies, according to several studies. These samples are rarely analysed for their ability to predict quality. Extreme caution is required to organise agricultural output surgically. Detecting high incidence rates in commercial production is difficult because of the unfair model's unpredictability, resulting in more difficulty in diagnosing reflex plant diseases. The proposed model is designed to identify the guava disease using convolutional neural networks (CNNs) and machine learning for classification. An autoencoder is used to divide the neural network design in the encoder and decoder. The linear support vector machine is used as a classification to analyse the outcomes of our experiments. Preliminary results from the suggested model indicate a remarkable degree of accuracy (97.5%).

Keywords: CNN feature extraction; guava disease; auto encoder preprocessing; data augmentation; plant disease detection.

DOI: 10.1504/IJES.2023.141930

International Journal of Embedded Systems, 2023 Vol.16 No.5/6, pp.354 - 363

Received: 16 Dec 2022
Accepted: 15 Sep 2023
Published online: 03 Oct 2024 *

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Title: Evaluation of CNN-based computer vision recommended treatments for recognised guava disease

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