"How sweet are your strawberries?": Predicting sugariness using non-destructive and affordable hardware.

Abstract

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

Plant phenotyping relevance

イチゴ果実の糖度（Brix）を非破壊・現場取得データから推定する手法を開発・検証し、複数モデルの精度をベンチマークしているため、植物形質取得が中心である。

Code and data availability

The paper states its collected datasets are deposited in the 4TU Data Repository (doi:10.4121/21864590), which is a paper-specific public phenotype dataset. However, no 4TU URL is present in the allowed_urls list, and the only allowed URLs (Frontiers article/supplementary page, Keras, Detectron2, CBI market pages) do不是

No evidence-backed public reproduction asset is currently recorded.