Sibiya, M.; Sumbwanyambe, M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering2019, 1, 119-131.
Sibiya, M.; Sumbwanyambe, M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering 2019, 1, 119-131.
Sibiya, M.; Sumbwanyambe, M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering2019, 1, 119-131.
Sibiya, M.; Sumbwanyambe, M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering 2019, 1, 119-131.
Abstract
Plant leaf diseases can affect the plants’ leaves to an extent that the plants can collapse and die completely. These diseases may drastically drop the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the Convolutional Neural Networks (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognized and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and the methodology used, expedite a quick and easy implementation of the system in practice. The developed model was able to recognize 3 different types of maize leaf diseases out of healthy leaves. The Northern Corn Leaf Blight (Exserohilum), Common Rust (Puccinia sorghi) and Gray Leaf Spot (Cerospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.
Keywords
Northern Corn Leaf Blight (Exserohilum); Gray Leaf Spot (Cerospora); Common Rust (Puccinia sorghi); Convolutional Neural Networks (CNN); Neuroph Studio
Subject
Biology and Life Sciences, Plant Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
27 May 2019
Commenter:
Antônio Teixeira Santana Neto
The commenter has declared there is no conflict of interests.
Comment:
Dear Malusi and Mbuyu,
My name is Antônio, I'm a student of Electrical Engineering at Federal University of Vicosa, Brazil. Recently, I read a paper of yours titled "A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases out of Healthy Leaves Using Convolutional Neural Networks" as I was looking for papers about plant diseases classification.
I've found very interesting the paper and I'd like to know if it's possible to access the maize leaves diseases database, so that we could contribute for each others research.
Let me know if we can colaborate.
Best Regards,
Antônio Teixeira
Federal University of Vicosa
Electrical Engineering Department - DEL/UFV
Commenter: Antônio Teixeira Santana Neto
The commenter has declared there is no conflict of interests.
My name is Antônio, I'm a student of Electrical Engineering at Federal University of Vicosa, Brazil. Recently, I read a paper of yours titled "A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases out of Healthy Leaves Using Convolutional Neural Networks" as I was looking for papers about plant diseases classification.
I've found very interesting the paper and I'd like to know if it's possible to access the maize leaves diseases database, so that we could contribute for each others research.
Let me know if we can colaborate.
Best Regards,
Antônio Teixeira
Federal University of Vicosa
Electrical Engineering Department - DEL/UFV