Open Access

ARTICLE

Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

Rabia Saleem¹, Jamal Hussain Shah^1,*, Muhammad Sharif¹, Ghulam Jillani Ansari²

1 COMSATS University Islamabad, Wah Campus, 47040, Pakistan
2 University of Education, Lahore, 54000, Pakistan

* Corresponding Author: Jamal Hussain Shah. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)

Computers, Materials & Continua 2021, 69(3), 3581-3601. https://doi.org/10.32604/cmc.2021.017700

Received 08 February 2021; Accepted 01 May 2021; Issue published 24 August 2021

Abstract

Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques. We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute, Multan, Pakistan. To evaluate the proposed model results, we compared the segmentation performance with the available state-of-the-art models, i.e., Vgg16, Vgg-19, and Unet. Furthermore, the proposed model's segmentation accuracy is 99.2% with a false negative rate (FNR) of 0.8%, which is much higher than the other models. We have concluded that by using a FrCNnet, the input image could learn better features that are more prominent and much specific, resulting in an improved and better segmentation performance and diseases’ identification. Accordingly, an automated approach helps pathologists and mango growers detect and identify those diseases.

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