Automatic Orange Fruit Disease Identification Using Visible Range Images

Peter, V. Joseph; Khan, Muhammad Asim; Luo, Huilan

doi:10.1007/978-981-15-5577-0_26

Cited by 3 publications

(2 citation statements)

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“…Wang et al [27] proposed an algorithm to predict the sugar content of citrus fruits and performed a classification of the sugar content using light in the visible spectrum. Similar approaches for sorting apples can be found in [5,9,10,28]; for tomatoes in [23,29]; for sorting watermelons in [30]; for palm oil fruit sorting in [31]; and dates in [32].…”

Section: Related Workmentioning

confidence: 90%

“…They report a sorting accuracy of 94.38%. Peter et al [23] proposed an automatic system for disease identification in infected fruits images. The approach is evaluated on three diseases of the navel orange fruits, namely Citrus canker, Citrus melanose, and Citrus black spot, achieving 93% accuracy using global color histogram, local binary patterns, and Halarick texture features.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Embedded Vision System for Online Monitoring and Sorting of Citrus Fruits

Nuño-Maganda,

Dávila-Rodríguez,

Hernández-Mier

et al. 2023

Electronics

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Citrus fruits are the second most important crop worldwide. One of the most important tasks is sorting, which involves manually separating the fruit based on its degree of maturity, and in many cases, involves a task carried out manually by human operators. A machine vision-based citrus sorting system can replace labor work for the inspection of fruit sorting. This article proposes a vision system for citrus fruit sorting implemented on a dedicated and efficient Field Programmable Gate Array (FPGA) hardware architecture coupled with a mechanical sorting machine, where the FPGA performs fruit segmentation and color and size classification. We trained a decision tree (DT) using a balanced dataset of reference images to perform pixel classification. We evaluate the segmentation task using a pixel accuracy metric, defined as the ratio between correctly segmented pixels produced by a DT and the total pixels in the reference image segmented offline using Otsu’s thresholding algorithm. The balance between correctly classified images by color or size and their corresponding labels of that color and size evaluates the color and size classification algorithms. Considering these metrics, the system reaches an accuracy of 97% for fruit segmentation, 94% for color classification, and 90% for size classification, running at 60 fps.

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