Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning

Early and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops w...

Full description

Bibliographic Details
Main Authors: Alberta Odamea Anim-Ayeko, Calogero Schillaci, Aldo Lipani
Format: Article
Language:English
Published: Elsevier 2023-08-01
Series:Smart Agricultural Technology
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2772375523000084
_version_ 1797838482664587264
author Alberta Odamea Anim-Ayeko
Calogero Schillaci
Aldo Lipani
author_facet Alberta Odamea Anim-Ayeko
Calogero Schillaci
Aldo Lipani
author_sort Alberta Odamea Anim-Ayeko
collection DOAJ
description Early and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops which have been affected. Machine learning and deep learning technology provide many solutions for the detection of the blight diseases in affected crops, and are common in the literature. However, explanation methods for such solutions are not common, but are necessary, considering some machine learning models are seen as black boxes. This study proposes a ResNet-9 model which detects the blight disease state of potato and tomato leaf images, which farmers can leverage. With the data obtained from the popular “Plant Village Dataset”, there were 3,990 initial training data samples. After augmenting the training set and a rigorous hyperparameter optimization procedure, the model was trained with these hyperparameter values, and examined on the test set, which contained 1,331 images. A test accuracy of 99.25%, 99.67% overall precision, 99.33% overall recall and 99.33% overall F1-score values were achieved. To fully understand the model, explanations for the proposed model were provided through saliency maps, which showed the reasoning behind the predictions of the model. It was observed that the ResNet-9 model considered the shape of the leaf, diseased areas present and general green areas of the leaf for its predictions and this makes us understand the model predictions better and see that the model behaves as expected. Our results could contribute to the testing and deployment of Convolutional Neural Network (CNN) models for classification of proximal sensing images of potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plant leaves. Further studies would benefit from this modeling framework and would have the chance to test several other variables to determine the leaf infections in an earlier stage for crop protection.
first_indexed 2024-04-09T15:42:38Z
format Article
id doaj.art-0a4b7518649f4bd095ba8a65158a0239
institution Directory Open Access Journal
issn 2772-3755
language English
last_indexed 2024-04-09T15:42:38Z
publishDate 2023-08-01
publisher Elsevier
record_format Article
series Smart Agricultural Technology
spelling doaj.art-0a4b7518649f4bd095ba8a65158a02392023-04-27T06:08:37ZengElsevierSmart Agricultural Technology2772-37552023-08-014100178Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learningAlberta Odamea Anim-Ayeko0Calogero Schillaci1Aldo Lipani2European Commission JRC, ItalyEuropean Commission JRC, Italy; Corresponding author.University College London, UKEarly and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops which have been affected. Machine learning and deep learning technology provide many solutions for the detection of the blight diseases in affected crops, and are common in the literature. However, explanation methods for such solutions are not common, but are necessary, considering some machine learning models are seen as black boxes. This study proposes a ResNet-9 model which detects the blight disease state of potato and tomato leaf images, which farmers can leverage. With the data obtained from the popular “Plant Village Dataset”, there were 3,990 initial training data samples. After augmenting the training set and a rigorous hyperparameter optimization procedure, the model was trained with these hyperparameter values, and examined on the test set, which contained 1,331 images. A test accuracy of 99.25%, 99.67% overall precision, 99.33% overall recall and 99.33% overall F1-score values were achieved. To fully understand the model, explanations for the proposed model were provided through saliency maps, which showed the reasoning behind the predictions of the model. It was observed that the ResNet-9 model considered the shape of the leaf, diseased areas present and general green areas of the leaf for its predictions and this makes us understand the model predictions better and see that the model behaves as expected. Our results could contribute to the testing and deployment of Convolutional Neural Network (CNN) models for classification of proximal sensing images of potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plant leaves. Further studies would benefit from this modeling framework and would have the chance to test several other variables to determine the leaf infections in an earlier stage for crop protection.http://www.sciencedirect.com/science/article/pii/S2772375523000084Deep learningResNet-9Blight diseaseSHAPPotato (Solanum tuberosum L.)Tomato (Solanum lycopersicum
spellingShingle Alberta Odamea Anim-Ayeko
Calogero Schillaci
Aldo Lipani
Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
Smart Agricultural Technology
Deep learning
ResNet-9
Blight disease
SHAP
Potato (Solanum tuberosum L.)
Tomato (Solanum lycopersicum
title Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
title_full Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
title_fullStr Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
title_full_unstemmed Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
title_short Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning
title_sort automatic blight disease detection in potato solanum tuberosum l and tomato solanum lycopersicum l 1753 plants using deep learning
topic Deep learning
ResNet-9
Blight disease
SHAP
Potato (Solanum tuberosum L.)
Tomato (Solanum lycopersicum
url http://www.sciencedirect.com/science/article/pii/S2772375523000084
work_keys_str_mv AT albertaodameaanimayeko automaticblightdiseasedetectioninpotatosolanumtuberosumlandtomatosolanumlycopersicuml1753plantsusingdeeplearning
AT calogeroschillaci automaticblightdiseasedetectioninpotatosolanumtuberosumlandtomatosolanumlycopersicuml1753plantsusingdeeplearning
AT aldolipani automaticblightdiseasedetectioninpotatosolanumtuberosumlandtomatosolanumlycopersicuml1753plantsusingdeeplearning