FruitProm: Probabilistic Maturity Estimation and Detection of Fruits and Vegetables

2025-10-28

AI Summary

Existing methods formulate fruit and vegetable ripeness estimation as discrete classification, contradicting its inherently continuous evolutionary nature and thereby introducing boundary ambiguity and information loss. To address this, we propose the first continuous probabilistic learning framework for ripeness estimation, reformulating RT-DETRv2 as a probabilistic detector. Our approach introduces a learnable distribution-output head that jointly optimizes object localization, ripeness-level classification, and uncertainty modeling. Specifically, it directly predicts a continuous probability distribution—parameterized by mean and variance—for ripeness, ensuring biological plausibility while enabling confidence-aware decision-making for robotic applications. Evaluated on a large-scale fruit-and-vegetable dataset, our method achieves 85.6% mAP, significantly improving both fine-grained ripeness-state assessment accuracy and prediction reliability compared to discrete alternatives.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Maturity estimation of fruits and vegetables is a critical task for agricultural automation, directly impacting yield prediction and robotic harvesting. Current deep learning approaches predominantly treat maturity as a discrete classification problem (e.g., unripe, ripe, overripe). This rigid formulation, however, fundamentally conflicts with the continuous nature of the biological ripening process, leading to information loss and ambiguous class boundaries. In this paper, we challenge this paradigm by reframing maturity estimation as a continuous, probabilistic learning task. We propose a novel architectural modification to the state-of-the-art, real-time object detector, RT-DETRv2, by introducing a dedicated probabilistic head. This head enables the model to predict a continuous distribution over the maturity spectrum for each detected object, simultaneously learning the mean maturity state and its associated uncertainty. This uncertainty measure is crucial for downstream decision-making in robotics, providing a confidence score for tasks like selective harvesting. Our model not only provides a far richer and more biologically plausible representation of plant maturity but also maintains exceptional detection performance, achieving a mean Average Precision (mAP) of 85.6% on a challenging, large-scale fruit dataset. We demonstrate through extensive experiments that our probabilistic approach offers more granular and accurate maturity assessments than its classification-based counterparts, paving the way for more intelligent, uncertainty-aware automated systems in modern agriculture

Problem

• Estimate continuous fruit maturity distribution using probabilistic learning
• Overcome discrete classification limitations in biological ripening processes
• Provide uncertainty-aware maturity assessments for agricultural robotics

Innovation

• Probabilistic head predicts continuous fruit maturity distribution
• Modifies RT-DETRv2 detector with uncertainty-aware architecture
• Simultaneously estimates mean maturity state and associated uncertainty