Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin's biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins.
Keywords: biophysical properties; deep learning; measurement; pumpkin; segmentation.