  Tomatoes are a major crop worldwide, and accurately classifying their
maturity is important for many agricultural applications, such as harvesting,
grading, and quality control. In this paper, the authors propose a novel method
for tomato maturity classification using a convolutional transformer. The
convolutional transformer is a hybrid architecture that combines the strengths
of convolutional neural networks (CNNs) and transformers. Additionally, this
study introduces a new tomato dataset named KUTomaData, explicitly designed to
train deep-learning models for tomato segmentation and classification.
KUTomaData is a compilation of images sourced from a greenhouse in the UAE,
with approximately 700 images available for training and testing. The dataset
is prepared under various lighting conditions and viewing perspectives and
employs different mobile camera sensors, distinguishing it from existing
datasets. The contributions of this paper are threefold:Firstly, the authors
propose a novel method for tomato maturity classification using a modular
convolutional transformer. Secondly, the authors introduce a new tomato image
dataset that contains images of tomatoes at different maturity levels. Lastly,
the authors show that the convolutional transformer outperforms
state-of-the-art methods for tomato maturity classification. The effectiveness
of the proposed framework in handling cluttered and occluded tomato instances
was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno
Annotated Tomato, as benchmarks. The evaluation results across these three
datasets demonstrate the exceptional performance of our proposed framework,
surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean
average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated
Tomato, respectively.
