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 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.