s (LOD) score at a particular marker exceeded that computed at the α = 0.05 level under null models generated via 1000 random permutations. 2.8 Data, software, and equipment instruction availability The image datasets, along with annotations, are publicly available through the USDA National Agricultural Library Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v. 4.5.0; R Core Team, 2025). Scripts to replicate the analyses, along with a list of materials for recreating the Berry- Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run- ning the image capture and analysis
Open resource ↗10.15482/USDA.ADC/29853332 · pdf-raw-page:8 lines:1-125Unverified paper record
BerryBox: An affordable computer vision system for postharvest phenotyping of cranberry and other small fruits
The Plant Phenome Journal · 1 Jul 2026 · 10.1002/ppj2.70090
Abstract
Abstract Fruit size, shape, color, and percent fruit rot are important quality traits for breeding cranberry ( Vaccinium macrocarpon Ait.). Image analysis can be used to measure these traits, but affordable hardware for standardized image capture and integrated user‐friendly software pipelines are lacking. Additionally, no image‐based method exists to estimate percent fruit rot, an otherwise tediously and subjectively measured trait. We created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images. Trained deep neural network models were highly accurate for segmenting sound fruit (F1 score: 99.4%) and detecting rotten fruit (F1 score: 98.5%). We applied the BerryBox to images of cranberries harvested across 3 years from a 156‐clone breeding population. Narrow‐sense heritability estimates of image‐based fruit color, shape, size, and percent fruit rot ranged from 0.37 to 0.95. Random subsampling showed that 25–30 berries per genotype were sufficient to describe the variation in the full dataset. We demonstrated the utility of BerryBox traits in a small‐scale genetic linkage mapping analysis, detecting significant marker–trait associations that coincided with those of traditionally measured traits. The BerryBox software was able to accurately segment fruit from images of blueberries without model retraining, showing its applicability to other similarly shaped fruits. The software pipeline and BerryBox materials and assembly instructions are publicly available for others to adopt for low‐cost image‐based phenotyping.
Plant phenotyping relevance
クランベリー等の果実形質と腐敗率を画像から抽出する低コスト撮像装置・ソフトウェアパイプラインを開発し、精度検証と他果実への適用性評価を行った、中心的な植物フェノタイピング手法研究である。
abstractWe created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images.
abstractTrained deep neural network models were highly accurate for segmenting sound fruit (F1 score: 99.4%) and detecting rotten fruit (F1 score: 98.5%).
Code and data availability
The paper explicitly states public availability of the annotated image datasets (USDA Ag Data Commons DOI), R analysis scripts, the BerryBox Python software package with pre-trained models, and the model training code, all with author-provided public URLs.
able through the USDA National Agricultural Library Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v. 4.5.0; R Core Team, 2025). Scripts to replicate the analyses, along with a list of materials for recreating the Berry- Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run- ning the image capture and analysis software pipeline is available as a Python package from the GitHub reposi- tory https://github.com/NeyhartLab/berryboxai. The package includes pre-trained models for berry segmentation and fruit rot detection, and the code is available from https://github.com/N
Open resource ↗github.com/neyhartj/BerryBox_FruitPhenotyping · pdf-raw-page:8 lines:1-125). Scripts to replicate the analyses, along with a list of materials for recreating the Berry- Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run- ning the image capture and analysis software pipeline is available as a Python package from the GitHub reposi- tory https://github.com/NeyhartLab/berryboxai. The package includes pre-trained models for berry segmentation and fruit rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a custom model using high-performance computing resources or the widely available Google Colab environment (Rippner et al., 2022). 3 RESULT
Open resource ↗github.com/NeyhartLab/berryboxai · pdf-raw-page:8 lines:1-125eyhartj/BerryBox_FruitPhenotyping. Software for run- ning the image capture and analysis software pipeline is available as a Python package from the GitHub reposi- tory https://github.com/NeyhartLab/berryboxai. The package includes pre-trained models for berry segmentation and fruit rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a custom model using high-performance computing resources or the widely available Google Colab environment (Rippner et al., 2022). 3 RESULTS 3.1 Deep learning model training The trained berry segmentation model achieved an overall accuracy of 98.9% and an F1 score of 99.4%. The fruit rot detection m
Open resource ↗github.com/NeyhartLab/berryboxai_training_public · pdf-raw-page:8 lines:1-125This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.