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.@en

Strawberries are profitable fruits, yet they have a short shelf life. Therefore, it is crucial to anticipate their quality and harvest them at the best time, which is vital not only for finding the appropriate market but also for minimizing food and economic waste. To this end, non-destructive strawberry quality measurements are useful. Much research is conducted on post-harvest strawberries: the fruits were only analyzed after harvesting and thus, these methods cannot be used to find a good time to harvest. Our research targets pre-harvest analysis for supporting the timing decisions of harvests. As such, we used an infield image dataset that was collected during the cultivation of strawberries. The images are labeled by quality assessments and measurements from post-harvest destructive tests. We evaluated deep learning for quality estimation and trained our algorithms to predict the ripeness, firmness, and sweetness of strawberries. Additionally, we applied depth estimation algorithms and shape inpainting models to estimate the size of strawberries using images. Our results demonstrate the feasibility of infield quality attribute prediction.@en