Sisfrutos Papaya: A Dataset for Detection and Classification of Diseases in Papaya

Moraes, Jairo Lucas de; Neto, Jorcy de Oliveira; Correia-Silva, Jacson Rodrigues; Paixão, Thiago M.; Badué, Claudine; Oliveira-Santos, Thiago; Souza, Alberto F. De

doi:10.1007/978-3-030-86340-1_3

Cited by 1 publication

(3 citation statements)

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“…The detection of diseases in Papaya fruits is an arduous task, even for experienced human specialists. According to Moraes et al [13], the accuracy of a human expert, tested on an image base such as the one used in this study, was only 67.3%, and in some diseases, such as sticky disease, the performance was below 50%.…”

Section: Resultsmentioning

confidence: 90%

“…Moraes et al [13] created a dataset of 15,179 images depicting 7 diseases/damages that affect papaya, including "anthracnose", "Phytophthora blight", "mechanical damage", "chocolate spot", "sticky disease", "physiological spot", and "black spot". The images were obtained in situ in a fruit packaging facility in a rural production environment and annotated to depict a single disease in each image.…”

Section: Related Studiesmentioning

confidence: 99%

“…Prior to the scope of this research, the sole publicly accessible dataset featuring a substantial number of papaya disease samples with precise annotations suitable for utilization in convolutional neural networks (CNNs) was Sisfrutos Papaya [13]. Nonetheless, this dataset's limitation stemmed from its labeling approach, which assigned only one disease per image even when the fruit displayed additional secondary diseases; this proved to be a limiting factor for practical applications.…”

Section: Image Acquisitionmentioning

confidence: 99%

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Yolo-Papaya: A Papaya Fruit Disease Detector and Classifier Using CNNs and Convolutional Block Attention Modules

et al. 2023

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Agricultural losses due to post-harvest diseases can reach up to 30% of total production. Detecting diseases in fruits at an early stage is crucial to mitigate losses and ensure the quality and health of fruits. However, this task is challenging due to the different formats, sizes, shapes, and colors that the same disease can present. Convolutional neural networks have been proposed to address this issue, but most studies use self-built datasets with few samples per disease, hindering reproducibility and comparison of techniques. To address these challenges, the authors proposed a novel image dataset comprising 23,158 examples divided into nine classes of papaya fruit diseases, and a robust papaya fruit disease detector called Yolo-Papaya based on the YoloV7 detector with the implementation of a convolutional block attention module (CBAM) attention mechanism. This detector achieved an overall mAP (mean average precision) of 86.2%, with a performance of over 98% in classes such as “healthy fruits” and “Phytophthora blight”. The proposed detector and dataset can be used in practical applications for fruit quality control and are consolidated as a robust benchmark for the task of papaya fruit disease detection. The image dataset and all source code used in this study are available to the academic community on the project page, enabling reproducibility of the study and advancement of research in this domain.

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Section: Resultsmentioning

confidence: 90%

Section: Related Studiesmentioning

confidence: 99%

Section: Image Acquisitionmentioning

confidence: 99%

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