Automated classification of citrus disease on fruits and leaves using convolutional neural network (CNN) generated features from hyperspectral images and machine learning classifiers

Authors: YADAV, PAPPU - University Of Florida; BURKS, THOMAS - University Of Florida; Qin, Jianwei - Tony Qin; Kim, Moon; FREDERICK, QUENTIN - University Of Florida; DEWDNEY, MEGAN - University Of Florida; RITENOUR, MARK - University Of Florida

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/22/2024
Publication Date: 2/8/2024
Citation: Yadav, P., Burks, T.F., Qin, J., Kim, M.S., Frederick, Q., Dewdney, M.M., Ritenour, M.A. 2024. Automated classification of citrus disease on fruits and leaves using convolutional neural network (CNN) generated features from hyperspectral images and machine learning classifiers. Journal of Applied Remote Sensing (JARS). 18 (1): Article e014512. https://doi.org/10.1117/1.JRS.18.014512.
DOI: https://doi.org/10.1117/1.JRS.18.014512

Interpretive Summary: Citrus black spot and canker are two significant diseases that pose quarantine threat, restrict market access, and cause economic losses for citrus growers. Early detection and management of groves infected with black spot or canker through fruit and leaf inspection can greatly benefit the citrus industry. This study developed an AI-based hyperspectral imaging and classification method for detection of fruits infected with black spot and leaves infected with canker. Hyperspectral reflectance images were collected in visible and near-infrared wavelength range from Valencia orange fruits and leaves with black spot, canker, and other common citrus diseases. Using convolutional neural network generated features and machine learning classifiers, classification accuracies were achieved over 92% for classifying the fruits with black spot and four other conditions and over 93% for classifying the leaves with canker and four other conditions. The method would benefit the citrus industry and regulatory agencies (e.g., FDA and USDA APHIS) in ensuring and enforcing the quality and safety standards for the citrus-related food and beverage products.

Technical Abstract: Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa, which poses a quarantine threat and can restrict market access for fruits. It manifests as lesions on the fruit surface and can result in premature fruit drops, leading to reduced yield. Another significant disease affecting citrus is canker, caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri), which causes economic losses for growers due to fruit drops and blemishes. Early detection and management of groves infected with CBS or canker through fruit and leaf inspection can greatly benefit the Florida citrus industry. However, manual inspection and classification of disease symptoms on fruits or leaves are labor-intensive and time-consuming processes. Therefore, there is a need to develop a computer vision system capable of autonomously classifying fruits and leaves, expediting disease management in the groves. This paper aims to demonstrate the effectiveness of convolutional neural network (CNN) generated features and machine learning (ML) classifiers for detection of CBS infected fruits and leaves with canker symptoms. A custom shallow CNN with RBF SVM achieved an overall accuracy of 92.1% for classifying fruits with CBS and four other conditions, while a custom VGG16 with RBF SVM could classify leaves with canker and other four conditions at an overall accuracy of 93%. These preliminary findings demonstrate the potential of utilizing hyperspectral imaging (HSI) systems for automated classification of citrus fruit and leaf diseases using shallow and deep CNN-generated features, along with ML classifiers.