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Pitaya or dragon fruit is a tropical fruit prized for its unusual look and several health advantages including a high vitamin and antioxidant content. Dragon fruit is becoming more and more popular worldwide which benefits emerging countries like Bangladesh, Vietnam, China, Thailand, Indonesia, Israel and India economically. The present study offers a very large collection of high resolution dragon fruit photos that will help the machine learning models in their determination of the ripeness and quality of the fruit. The dataset was created with the utmost care and guidance from specialists over a period of four months from three different locations in Bangladesh. The collection is of great importance in facilitating the operations of dragon fruit production by giving resources for robotic harvesting, quality evaluation, and packing systems etc. We performed quality assessment using the Nasnet Mobile, DenseNet121 and MobileNetV2 models which yielded test accuracies of 94.89%, 96.06% and 97% respectively. MobileNetV2 model got the maximum accuracy of 97.00%. It showed a recall of 0.97, F1-score of 0.97 and precision of 0.97 which means very trustworthy and even performance. The data of this study along with the outcomes of the deep learning models besides being a valuable asset for researchers and practitioners in the industry also lead to the establishment of automated systems for the dragon fruit production.

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A Deep Learning-Driven Approach to Automated Dragon Fruit Quality Grading

  • 会议论文

  • 发表日期：

    2026年

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  • Sarasij Majee,
  • Ayan Das,
  • Pritoma Saha,
  • Subhadip Das,
  • Shruti Pandey,
  • Suptasish Sarkar,
  • Kousik Roy

摘要

Pitaya or dragon fruit is a tropical fruit prized for its unusual look and several health advantages including a high vitamin and antioxidant content. Dragon fruit is becoming more and more popular worldwide which benefits emerging countries like Bangladesh, Vietnam, China, Thailand, Indonesia, Israel and India economically. The present study offers a very large collection of high resolution dragon fruit photos that will help the machine learning models in their determination of the ripeness and quality of the fruit. The dataset was created with the utmost care and guidance from specialists over a period of four months from three different locations in Bangladesh. The collection is of great importance in facilitating the operations of dragon fruit production by giving resources for robotic harvesting, quality evaluation, and packing systems etc. We performed quality assessment using the Nasnet Mobile, DenseNet121 and MobileNetV2 models which yielded test accuracies of 94.89%, 96.06% and 97% respectively. MobileNetV2 model got the maximum accuracy of 97.00%. It showed a recall of 0.97, F1-score of 0.97 and precision of 0.97 which means very trustworthy and even performance. The data of this study along with the outcomes of the deep learning models besides being a valuable asset for researchers and practitioners in the industry also lead to the establishment of automated systems for the dragon fruit production.