Resumo

KHALID, H.. Modern techniques in detecting, identifying and classifying fruits according to the developed machine learning algorithm. J. appl. res. technol [online]. 2024, vol.22, n.2, pp.219-229.  Epub 04-Ago-2025. ISSN 2448-6736.  https://doi.org/10.22201/icat.24486736e.2024.22.2.2269.

Recent developments in machine vision have opened a wide range of applications, and farming is no exception. Deep learning (DL) has a wide range of applications because of its capacity to extract robust features from photos. The shape, color, and feel of many fruit species make it difficult to discover and classify fruits. When examining the effects of artificial intelligence on fruit identification and classification, the Author noted that, up until 2018, the majority of approaches relied on traditional machine learning (ML) techniques, while just a few ways took use of DL techniques for recognizing fruits and categorization. In this paper, the Author thoroughly covered the datasets that many academics utilized, the useful descriptors, the application of the model, and the difficulties of utilizing DL to identify and classify fruits. Finally, the Author compiled the outcomes of various DL techniques used in earlier research to identify and categorize fruits. This work examines the use of models based on DL for fruit categorization and recognition in recent studies. In order to make it simpler for beginning agricultural researchers to comprehend the importance of ML in the agricultural domain, the Author have developed a DL model for apple categorization using the well-known dataset "Fruit 360" starting from scratch. The recently proposed model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%).

Palavras-chave : Deep learning techniques; machine learning; fruit detection and classification; Fruit 360.

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