Tomato maturity recognition with convolutional transformers

Abstract

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. This work can potentially improve the efficiency and accuracy of tomato harvesting, grading, and quality control processes.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

“…In agricultural production, crop maturity delineates the physiological stage of maturation, with maize maturity typically discerned through the assessment of indicators such as the milk line on maize kernels, moisture content, and leaf color. It serves as a pivotal determinant for yield formation and concurrently represents a crucial trait for assessing the growth stages of crops [2,3]. For agricultural decision makers, this parameter is a crucial indicator for selecting superior varieties [4].…”

YOLO-Granada: A Lightweight Attentioned Yolo forPomegranates Fruit Detection

“…The results show that the YOLO algorithm constructs a tea bud recognition model and realizes the highest accuracy of classification for four kinds of tea, which proves the superiority of the YOLO target detection algorithm, and also provides theoretical support for mechanical intelligent tea picking. In addition, Reference 18 introduces a novel approach employing a convolutional neural network and transformer for tomato ripeness classification, supported by the KUTomaData dataset, demonstrating its effectiveness in this task. Furthermore, Reference 19 tackles the challenge of intelligent online yield estimation for tomatoes in artificial lighting-based plant factories (PFAL) with an enhanced YOLOv3 deep learning model achieving 99.3% mean average precision (mAP).…”