TomatoScan: An Android-based application for quality evaluation and ripening determination of tomato fruit

Sherafati, Avin; Mollazade, Kaveh; Saba, Mahmoud Koushesh; Vesali, Farshad

doi:10.1016/j.compag.2022.107214

Cited by 8 publications

(2 citation statements)

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“…These methods, built upon neural networks, are used to analyze large-scale datasets and derive insightful patterns for the precise detection and monitoring of tomatoes. Sherafati et al 29 proposed a framework for assessing the ripeness of tomatoes from RGB images. Sladojevic et al 28 utilized transfer learning to detect and classify tomato diseases.…”

Section: Modern Computer Vision Methods For Precision Agriculturementioning

confidence: 99%

Tomato maturity recognition with convolutional transformers

Khan,

Hassan,

Shafay

et al. 2023

Sci Rep

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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.

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Section: Modern Computer Vision Methods For Precision Agriculturementioning

confidence: 99%

Tomato maturity recognition with convolutional transformers

Khan,

Hassan,

Shafay

et al. 2023

Sci Rep

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“…For instance, Sherafati et al. [ 23 ] developed an Android APP named TomatoScan, which utilizes a multilayer perceptron network to predict the ripening stages of tomatoes. Rimon et al.…”

Section: Introductionmentioning

confidence: 99%

StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust

Liu,

Chen,

et al. 2024

Plant Phenomics

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Wheat stripe rust poses a marked threat to global wheat production. Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases. In this study, we propose a practical solution using mobile-based deep learning and model-assisted labeling. StripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds. Additionally, StripeRust-Pocket facilitates image acquisition, result storage, organization, and sharing. The underlying model employed by StripeRust-Pocket, called StripeRustNet, is a balanced lightweight 2-stage model. The first stage utilizes MobileNetV2-DeepLabV3+ for leaf segmentation, followed by ResNet50-DeepLabV3+ in the second stage for lesion segmentation. Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area. StripeRustNet achieves 98.65% mean intersection over union (MIoU) for leaf segmentation and 86.08% MIoU for lesion segmentation. Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores. To address the challenges in manual labeling, we introduce a 2-stage labeling pipeline that combines model-assisted labeling, manual correction, and spatial complementarity. We apply this pipeline to our self-collected dataset, reducing the annotation time from 20 min to 3 min per image. Our method provides an efficient and practical solution for wheat stripe rust severity assessments, empowering wheat breeders and pathologists to implement timely disease management. It also demonstrates how to address the “last mile” challenge of applying computer vision technology to plant phenomics.

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