Open Access

ARTICLE

Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

by Nazeer Muhammad¹, Rubab², Nargis Bibi³, Oh-Young Song⁴, Muhammad Attique Khan^5,*, Sajid Ali Khan⁶

1 Department of IT & CS, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, 22620, Pakistan
2 COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
3 Fatima Jinnah Women University, Rawalpindi, 44000, Pakistan
4 Software Department, Sejong University, Seoul, South Korea
5 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
6 Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan

* Corresponding Author: Muhammad Attique Khan. Email:

(This article belongs to the Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)

Computers, Materials & Continua 2021, 66(2), 2199-2216. https://doi.org/10.32604/cmc.2020.012257

Received 22 June 2020; Accepted 01 October 2020; Issue published 26 November 2020

Abstract

Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully automated method for plant disease detection and recognition using deep neural networks. In the proposed method, AlexNet and VGG19 CNNs are considered as pre-trained architectures. It is capable to obtain the feature extraction of the given data with fine-tuning details. After convolutional neural network feature extraction, it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor, Support Vector Machine, Probabilistic Neural Network, Fuzzy logic, and Artificial Neural Network. The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step. The achieved average accuracy of our method is more than 96% and outperforms the recent techniques.

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Citations