The non-destructive detection of insect-infested fruit is a topic of great interest for biosecurity and for the quality management of horticultural supply chains.
In the case of the Queensland fruit fly (Qfly, Bactrocera tryoni), a highly damaging pest for international horticultural trade, the difficulty of identification stems from the fact that oviposition marks are often minimal and hard to distinguish with the naked eye from natural pigmentation or mild mechanical damage.
A recent study proposed an innovative approach that integrates advanced optical imaging techniques with artificial intelligence algorithms to reliably detect oviposition punctures on fresh cherries.
Through the use of hyperspectral images (HSI), researchers identified 730 nm as the optimal wavelength to discriminate oviposition lesions from other superficial irregularities on the fruit.
This choice is based on the observation that, at 730 nm, damaged areas exhibit distinctive characteristics: dark spots with a whitish centre, while pigmentation and non-pest-related defects become less visible.
Building on this information, the researchers developed the first high-resolution, single-wavelength NIR image library dedicated to Qfly detection, containing more than 1,700 images acquired using a microscope and a modified near-infrared camera.
Oviposition spots were manually annotated, generating over 13,000 bounding boxes and more than 120,000 training patches, a dataset unprecedented in size and specificity.
The novelty lies not only in the acquisition strategy but especially in the analytical methodology.
The researchers developed a machine learning (ML) framework known as the Bounding Box Histogram Fusion Classifier (BBHFC), which uses the output of a YOLOv3 model trained to identify oviposition punctures.
Instead of relying on a decision threshold applied directly to the object detector’s predictions (the YOLO-IP approach), BBHFC converts YOLO-generated bounding boxes into feature vectors based on confidence and class histograms, which are then processed by traditional classifiers such as decision trees, random forests or SVMs.
This strategy drastically reduces sensitivity to confidence thresholds and enables more robust performance in identifying infested fruit.
Results show that the BBHFC system significantly outperforms YOLO-IP in terms of accuracy, sensitivity, and specificity, achieving F1 scores above 0.93 at the image level and over 0.89 at the fruit level.
The decision tree model, in particular, achieved an optimal balance between sensitivity (0.9689) and specificity (0.9544).
The comparison with human visual inspection yielded striking results: five inspectors, evaluating 155 artificially infested cherries randomly mixed with control fruit, achieved an average accuracy of 60%, whereas the BBHFC model exceeded 89%.
This gap highlights the enormous potential of NIR imaging technologies combined with AI algorithms to support or complement human evaluation during grading operations, phytosanitary inspections, and pre- and post-border checks, activities that are costly yet essential for international trade.
In conclusion, the technical and operational feasibility of single-wavelength NIR imaging proves excellent, particularly as a rapid and cost-effective alternative to hyperspectral imaging, which is currently difficult to apply in industrial settings due to cost and acquisition time.
The BBHFC framework stands out for its modularity and scalability, opening the way for potential integration into optical sorting systems currently used in fruit packing facilities.
Future developments will focus on reducing false positives, automating labelling procedures, and assessing the technology in high-speed industrial workflows, essential prerequisites for future commercial adoption.
Source: Yazdani, M., Bao, D., Zhou, J., Wang, A., & van Klinken, R. D. (2025). Single-Wavelength Near-Infrared Imaging and Machine Learning for Detecting Queensland Fruit Fly Damage in Cherries. Smart Agricultural Technology, 101090. https://doi.org/10.1016/j.atech.2025.101090
Source images: Yazdani et al., 2025
Andrea Giovannini
University of Bologna (IT)
15 May 2026
In Apulia, the 2026 cherry season starts under pressure from rain, cracking, Bigarreau damage and falling prices. Early varieties are struggling, while growers and traders focus on Ferrovia cherries, crop covers and shared strategies to protect quality, income and market value.
26 Jun 2026
A three-year study in Poland compares organic and conventional sour cherries, showing that cultivar, climate and season affect fruit quality more than orchard management alone. Oblačinska stands out as the most promising cultivar for high-quality organic production.
31 Jul 2026
An open-source, low-cost dendrometer system tracks trunk growth, water stress and irrigation needs in apple, sour cherry and peach orchards. Built on Arduino, it supports precise, sustainable water management and makes plant monitoring more accessible at commercial scale.
31 Jul 2026
Poland’s cherry season is delivering barely 50% of the 2025 record crop. Frost, drought, extreme heat and heavy imports are squeezing prices and sales, while growers rely on cold storage, premium quality and pick-your-own events to protect margins in a brutal market.
