Classifying early apple scab infections in multispectral imagery using convolutional neural networks

Bleasdale, Alex and Whyatt, Duncan (2024) Classifying early apple scab infections in multispectral imagery using convolutional neural networks. Artificial Intelligence in Agriculture. (In Press)

Abstract

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.