Classifying early apple scab infections in multispectral imagery using convolutional neural networks
Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. Near-infrared (NIR) imagery can display apple scab symptoms earlier and at a greater severity than v...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721724000357 |
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| author | Alexander J. Bleasdale J. Duncan Whyatt |
| author_facet | Alexander J. Bleasdale J. Duncan Whyatt |
| author_sort | Alexander J. Bleasdale |
| collection | DOAJ |
| description | Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. Near-infrared (NIR) imagery can display apple scab symptoms earlier and at a greater severity than visible-spectrum (RGB) imagery. Early apple scab diagnosis based on NIR imagery may be automated using deep learning convolutional neural networks (CNNs). CNN models have previously been used to classify a range of apple diseases accurately but have primarily focused on identifying late-stage rather than early-stage detection. This study fine-tunes CNN models to classify apple scab symptoms as they progress from the early to late stages of infection using a novel multispectral (RGB-NIR) time series created especially for this purpose.This novel multispectral dataset was used in conjunction with a large Apple Disease Identification (ADID) dataset created from publicly available, pre-existing disease datasets. This ADID dataset contained 29,000 images of infection symptoms across six disease classes. Two CNN models, the lightweight MobileNetV2 and heavyweight EfficientNetV2L, were fine-tuned and used to classify each disease class in a testing dataset, with performance assessed through metrics derived from confusion matrices. The models achieved scab-prediction accuracies of 97.13 % and 97.57 % for MobileNetV2 and EfficientNetV2L, respectively, on the secondary data but only achieved accuracies of 74.12 % and 78.91 % when applied to the multispectral dataset in isolation. These lower performance scores were attributed to a higher proportion of false-positive scab predictions in the multispectral dataset. Time series analyses revealed that both models could classify apple scab infections earlier than the manual classification techniques, leading to more false-positive assessments, and could accurately distinguish between healthy and infected samples up to 7 days post-inoculation in NIR imagery. |
| format | Article |
| id | doaj-art-c9f7f27627e0434aa796e2bc3ec5abd8 |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-c9f7f27627e0434aa796e2bc3ec5abd82025-01-11T06:41:51ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-03-011513951Classifying early apple scab infections in multispectral imagery using convolutional neural networksAlexander J. Bleasdale0J. Duncan Whyatt1Corresponding author.; Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YW, United KingdomLancaster Environment Centre, Lancaster University, Lancaster LA1 4YW, United KingdomMultispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. Near-infrared (NIR) imagery can display apple scab symptoms earlier and at a greater severity than visible-spectrum (RGB) imagery. Early apple scab diagnosis based on NIR imagery may be automated using deep learning convolutional neural networks (CNNs). CNN models have previously been used to classify a range of apple diseases accurately but have primarily focused on identifying late-stage rather than early-stage detection. This study fine-tunes CNN models to classify apple scab symptoms as they progress from the early to late stages of infection using a novel multispectral (RGB-NIR) time series created especially for this purpose.This novel multispectral dataset was used in conjunction with a large Apple Disease Identification (ADID) dataset created from publicly available, pre-existing disease datasets. This ADID dataset contained 29,000 images of infection symptoms across six disease classes. Two CNN models, the lightweight MobileNetV2 and heavyweight EfficientNetV2L, were fine-tuned and used to classify each disease class in a testing dataset, with performance assessed through metrics derived from confusion matrices. The models achieved scab-prediction accuracies of 97.13 % and 97.57 % for MobileNetV2 and EfficientNetV2L, respectively, on the secondary data but only achieved accuracies of 74.12 % and 78.91 % when applied to the multispectral dataset in isolation. These lower performance scores were attributed to a higher proportion of false-positive scab predictions in the multispectral dataset. Time series analyses revealed that both models could classify apple scab infections earlier than the manual classification techniques, leading to more false-positive assessments, and could accurately distinguish between healthy and infected samples up to 7 days post-inoculation in NIR imagery.http://www.sciencedirect.com/science/article/pii/S2589721724000357Apple scabPlant disease classificationMultispectral imagingConvolutional neural networkFine-tuningEarly detection |
| spellingShingle | Alexander J. Bleasdale J. Duncan Whyatt Classifying early apple scab infections in multispectral imagery using convolutional neural networks Artificial Intelligence in Agriculture Apple scab Plant disease classification Multispectral imaging Convolutional neural network Fine-tuning Early detection |
| title | Classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| title_full | Classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| title_fullStr | Classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| title_full_unstemmed | Classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| title_short | Classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| title_sort | classifying early apple scab infections in multispectral imagery using convolutional neural networks |
| topic | Apple scab Plant disease classification Multispectral imaging Convolutional neural network Fine-tuning Early detection |
| url | http://www.sciencedirect.com/science/article/pii/S2589721724000357 |
| work_keys_str_mv | AT alexanderjbleasdale classifyingearlyapplescabinfectionsinmultispectralimageryusingconvolutionalneuralnetworks AT jduncanwhyatt classifyingearlyapplescabinfectionsinmultispectralimageryusingconvolutionalneuralnetworks |