AppleV: a dataset for Apple fruit volume estimation

Abstract

The apple is among the most widely consumed fruits globally due to its high nutritional value and longer shelf life. Consequently, there is a need to monitor post-harvest health, which has important applications in grading, packaging and transportation. These tasks require the accurate computation and prediction of fruit size; however, there is a lack of publicly available datasets that the researcher can use to generalize it over apples. To bridge this gap, we introduce the AppleV dataset, which consists of images of 200 apples taken from different angles and sums up to 2000 images with their corresponding volume measurement. This work also explores three traditional approaches(Analytical method, Support Vector Machine Regression(SVMR), and Dynamic weighting) and five deep learning approaches(Custom model, EfficientNetV2S, VGG16, Xception, and ResNet50) for volume estimation. Traditional approaches use handcrafted features to estimate volume, whereas deep learning approaches learn these features automatically. To mitigate the risk of overfitting caused by the large number of trainable parameters in state-of-the-art deep learning models, pre-trained models trained on the ImageNet dataset are utilized. The results indicate that while the custom model demonstrated superior performance on the training data, ResNet50 outperformed all other methods on the AppleV dataset in terms of validation data accuracy. This work tries to offer a standard dataset specifically for the volume estimation task and aims to advance agricultural technology by automating volume estimation for apples based on image data, facilitating improved quality assessment and has various applications in industries such as grading and sorting.