Advanced phenotyping in tomato fruit classification through artificial intelligence
ABSTRACT: The tomato (Solanum lycopersicum L.) plays a vital role in global agriculture and is a model organism in genetic studies. Visual classification of tomatoes for genetic improvement programs faces challenges due to variety diversity, uneven ripening, external damages, and evaluator subjectiv...
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Universidade de São Paulo
2024-11-01
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| Series: | Scientia Agricola |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162025000101101&lng=en&tlng=en |
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| author | Sandra Eulália Santos Faria Alcinei Místico Azevedo Nayany Gomes Rabelo Varlen Zeferino Anastácio Valentina de Melo Maciel Deltimara Viana Matos Elias Barbosa Rodrigues Phelipe Souza Amorim Janete Ramos da Silva Fernanda de Souza Santos |
| author_facet | Sandra Eulália Santos Faria Alcinei Místico Azevedo Nayany Gomes Rabelo Varlen Zeferino Anastácio Valentina de Melo Maciel Deltimara Viana Matos Elias Barbosa Rodrigues Phelipe Souza Amorim Janete Ramos da Silva Fernanda de Souza Santos |
| author_sort | Sandra Eulália Santos Faria |
| collection | DOAJ |
| description | ABSTRACT: The tomato (Solanum lycopersicum L.) plays a vital role in global agriculture and is a model organism in genetic studies. Visual classification of tomatoes for genetic improvement programs faces challenges due to variety diversity, uneven ripening, external damages, and evaluator subjectivity. Recent advances in the field of computational resources, such as image phenotyping have enabled pre- and post-harvest assessments that are both fast and precise. This study aimed to classify tomato fruits based on shape, group, color, and defects using Convolutional Neural Networks (CNNs). The performance of five architectures - VGG16, InceptionV3, ResNet50, EfficientNetB3, and InceptionResNetV2 was evaluated to identify and determine the most efficient one for this classification. The research considered ten hybrids and their five parental lines. The experiment was conducted in the field, and images of ripe fruits were acquired using a portable mini studio. The ExpImage package in R software was used for fruit individualization by image and to aid in creating a synthetic database for network training. Images were grouped according to their classifications in terms of shape, color, groups, and defects. The InceptionResNetV2 architecture was the most efficient, achieving metrics such as precision and recall exceeding 93 % for most analyzed variables, and shorter classification times. This study advances the understanding of CNN applications in agriculture and research and provides valuable guidelines for optimizing classification tasks in distinct types of fruits. |
| format | Article |
| id | doaj-art-54c26820c02f4e7b84fa5f83d75c8cf1 |
| institution | Kabale University |
| issn | 1678-992X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Scientia Agricola |
| spelling | doaj-art-54c26820c02f4e7b84fa5f83d75c8cf12024-12-17T07:47:47ZengUniversidade de São PauloScientia Agricola1678-992X2024-11-018210.1590/1678-992x-2024-0115Advanced phenotyping in tomato fruit classification through artificial intelligenceSandra Eulália Santos Fariahttps://orcid.org/0000-0001-8773-7539Alcinei Místico Azevedohttps://orcid.org/0000-0001-5196-0851Nayany Gomes Rabelohttps://orcid.org/0009-0001-9018-6640Varlen Zeferino Anastáciohttps://orcid.org/0009-0000-0078-6806Valentina de Melo Macielhttps://orcid.org/0009-0009-1787-1404Deltimara Viana Matoshttps://orcid.org/0009-0001-1989-8389Elias Barbosa Rodrigueshttps://orcid.org/0000-0003-4837-8961Phelipe Souza Amorimhttps://orcid.org/0009-0003-5247-6975Janete Ramos da Silvahttps://orcid.org/0000-0001-6168-3368Fernanda de Souza Santoshttps://orcid.org/0009-0002-8297-3430ABSTRACT: The tomato (Solanum lycopersicum L.) plays a vital role in global agriculture and is a model organism in genetic studies. Visual classification of tomatoes for genetic improvement programs faces challenges due to variety diversity, uneven ripening, external damages, and evaluator subjectivity. Recent advances in the field of computational resources, such as image phenotyping have enabled pre- and post-harvest assessments that are both fast and precise. This study aimed to classify tomato fruits based on shape, group, color, and defects using Convolutional Neural Networks (CNNs). The performance of five architectures - VGG16, InceptionV3, ResNet50, EfficientNetB3, and InceptionResNetV2 was evaluated to identify and determine the most efficient one for this classification. The research considered ten hybrids and their five parental lines. The experiment was conducted in the field, and images of ripe fruits were acquired using a portable mini studio. The ExpImage package in R software was used for fruit individualization by image and to aid in creating a synthetic database for network training. Images were grouped according to their classifications in terms of shape, color, groups, and defects. The InceptionResNetV2 architecture was the most efficient, achieving metrics such as precision and recall exceeding 93 % for most analyzed variables, and shorter classification times. This study advances the understanding of CNN applications in agriculture and research and provides valuable guidelines for optimizing classification tasks in distinct types of fruits.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162025000101101&lng=en&tlng=enSolanum lycopersicum Limage analysisdeep learningtomato breedingneural networks |
| spellingShingle | Sandra Eulália Santos Faria Alcinei Místico Azevedo Nayany Gomes Rabelo Varlen Zeferino Anastácio Valentina de Melo Maciel Deltimara Viana Matos Elias Barbosa Rodrigues Phelipe Souza Amorim Janete Ramos da Silva Fernanda de Souza Santos Advanced phenotyping in tomato fruit classification through artificial intelligence Scientia Agricola Solanum lycopersicum L image analysis deep learning tomato breeding neural networks |
| title | Advanced phenotyping in tomato fruit classification through artificial intelligence |
| title_full | Advanced phenotyping in tomato fruit classification through artificial intelligence |
| title_fullStr | Advanced phenotyping in tomato fruit classification through artificial intelligence |
| title_full_unstemmed | Advanced phenotyping in tomato fruit classification through artificial intelligence |
| title_short | Advanced phenotyping in tomato fruit classification through artificial intelligence |
| title_sort | advanced phenotyping in tomato fruit classification through artificial intelligence |
| topic | Solanum lycopersicum L image analysis deep learning tomato breeding neural networks |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162025000101101&lng=en&tlng=en |
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