Computer Vision and Field Validation of an Artificial Intelligence-Based Tomato Grader for Large-Scale Production in Northeastern Thailand

Abstract

Tomato (Solanum lycopersicum L.) quality grading based on visual inspection often yields inconsistent results and reduces market value. The 'Perfect Gold 111' variety presents distinct morphological traits, including a characteristic green-to-red color transition, specific calyx structure, and defect patterns such as greenback distribution and suberization. These characteristics differ substantially from internationally studied cultivars, rendering generic pre-trained models insufficient for accurate grading under the Thai TACFS 1503–2007 standard. This study developed an automated grading model for 'Perfect Gold 111' tomatoes using a deep learning model based on a flow-based (node-based) architecture integrated with the Robot Operating System (ROS) framework and implemented on the CiRA CORE platform. A total of 220 samples were collected and graded according to the TACFS 1503–2007 standard. Top and side-view images were used to create a dataset comprising 165 tomatoes for training and 55 for testing. Model performance was evaluated using Precision, Recall, F1-Score, and Accuracy, and was compared with manual grading performed by farmers. The AI model achieved 80.00% of accuracy, outperforming farmer grading, which achieved 52.72% accuracy. In addition, the model reduced misclassification among visually similar grades and provided consistent, quantitative assessments of color, shape, and defects. These findings highlight the potential of AI-based grading systems to improve quality consistency, reduce labor, and support automated postharvest sorting for both smallholder and industrial tomato production.

Plant phenotyping relevance

トマトの色・形状・欠陥という観察可能な器官形質を画像から抽出し、深層学習による自動等級判定法を開発・検証しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper describes a 220-tomato image dataset and a CiRA CORE deep learning grading model, but contains no public deposit, availability statement, or authors' URL for the dataset, images, trained model, or code. No qualifying paper-specific public asset exists, and allowed_urls is empty.

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