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.

arXiv comment23 pages, 6 figures and 8 Tables

References in corpus (8)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• SSD: Single Shot MultiBox Detector
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
• YOLACT++: Better Real-time Instance Segmentation
• Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards
• Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
• Swin-transformer-yolov5 For Real-time Wine Grape Bunch Detection
• Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens