By providing objective, high-resolution data, Spotibot represents the first easy-to-use tool for rapid phenotyping in roses, offering breeders a transformative way to select resistant cultivars more accurately and efficiently.
Roses belong to the Rosaceae family, alongside apples, almonds, and strawberries, and hold enormous economic and cultural significance. Global cut rose production exceeded $3 billion in 2022, with the Netherlands leading exports and the U.S. as the largest importer. Roses, cherished worldwide for their beauty and cultural value, face a persistent threat from Botrytis cinerea, the grey mould fungus responsible for devastating post-harvest losses. In roses, the pathogen causes necrotic brown lesions that rapidly spread through spores, especially during harvest and post-harvest storage. No complete resistance has been identified, and existing defense is controlled by multiple genes with small effects. Because of this complexity, precise and objective phenotyping is essential to advance breeding for Botrytis resistance.
A study (DOI: 10.1016/j.plaphe.2025.100029) published in Plant Phenomics on 19 March 2025 by Dan Jeric Arcega Rustia’s team, Wageningen University & Research, opens new opportunities for precision rose breeding by delivering rapid, objective, and scalable phenotyping.
The study first optimized deep learning models for petal and Botrytis lesion segmentation to enable rapid, objective disease phenotyping in roses. Several YOLOv8 segmentation models were tested, and results showed that the petal segmentation model achieved high accuracy regardless of model size, with YOLOv8n-seg selected for deployment due to its fast inference time and stable performance. For lesion detection, YOLOv8s-seg achieved the highest F1-score, but YOLOv8n-seg was ultimately chosen for mobile use because of its much lower inference time, which is critical when analyzing multiple petals per image. Although the models performed well across colors, orange petals posed difficulties because lesions were less visually distinct, while red, pink, and white petals were more accurately segmented. Speed benchmarking demonstrated that Spotibot could process 4,155 images in 2.7 hours on a GPU-equipped PC, compared with over 7–11 hours on standard smartphones, which still represents a major improvement over manual scoring that typically takes 23 hours. Qualitative evaluation confirmed that the algorithm consistently detected lesions, with only minor boundary errors or occasional misidentification. To validate accuracy, subjective and objective results were compared using Spearman correlation, revealing strong positive relationships across lesion area, diameter, and lesion-to-petal ratios, with coefficients as high as 0.88. These correlations held across petal colors, although orange petals again showed lower agreement. Finally, a two-way ANOVA of 212 genotypes demonstrated that objective data captured stronger differences among cultivars than subjective scores, allowing clearer discrimination between resistant and susceptible genotypes. Moreover, when challenged with different Botrytis isolates, objective measurements enabled ranking of genotypes and differentiation between aggressive and mild isolates. Overall, the optimized models and Spotibot app proved capable of delivering fast, accurate, and reproducible lesion quantification, enhancing the precision of resistance screening in rose breeding.
Breeding programs, which often require 7–20 years to release new cultivars, can now accelerate selection for Botrytis resistance. The app’s accessibility on smartphones eliminates the need for costly equipment and makes it feasible for breeders worldwide to incorporate AI-driven screening into their pipelines. Beyond roses, Spotibot’s approach may be adapted for other crops where quantitative resistance traits are controlled by subtle genetic effects. In addition to breeding, the tool could improve post-harvest quality management by enabling early detection of infection and minimizing losses during storage and transport.
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References
DOI
10.1016/j.plaphe.2025.100029
Original URL
https://doi.org/10.1016/j.plaphe.2025.100029
Funding information
This work is part of a public-private-partnership project called “Variation in S gene and S gene dosage to increase Botrytis resistance in rose and strawberry” (Project number: LWV21.027) funded by the Dutch Topsector for Knowledge and Innovation and co-funded by the corresponding industry partners Dümmen Orange, De Ruiter Innovations, Interplant, Meilland International and United Selections.
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.