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Abstract
Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.
Highlights
Introduction published maps and institutional affilAccording to the FAO (FAOSTAT 2019) [1], world citrus fruit production is estimated to be at 157.98 million of tons, with oranges accounting for more than half of the total.Producers seek to produce superior fruits at a cheaper cost that are free of any disease insects and pathogens; this task can be accomplished through the use of appropriate mechanized standards and predictive maintenance techniques [2]
The objective of this paper is to develop a deep learning model that classifies the disease according to the severity level and to identify the disease-affected area of the citrus fruit
The results demonstrate that the accuracy of the disease severity level of citrus fruits was assessed as low severity (95.9%), high severity (99.7%), medium severity
Summary
Producers seek to produce superior fruits at a cheaper cost that are free of any disease insects and pathogens; this task can be accomplished through the use of appropriate mechanized standards and predictive maintenance techniques [2]. Fruit diseases create a substantial danger to modern farming production of citrus. The citrus sector needs early and automatic identification of diseases during post-harvesting since a few contaminated fruits might disseminate the disease to the entire sequence during processing or shipment. The severity of the disease is a crucial parameter for determining the extent of the disease and affects yield production. The ability to diagnose disease severity quickly and accurately would help to prevent production deficits; disease severity has been previously determined by trained professionals by visually inspecting plant tissues. The high cost and limited efficiency iations
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