Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations

Abstract

Fruit quality assessment is vital for ensuring consumer satisfaction and marketability in agriculture. This study explores deep learning techniques for assessing fruit quality, focusing on practical deployment in resource-constrained environments. Two approaches were compared: training a convolutional neural network (CNN) from scratch and fine-tuning a pre-trained MobileNetV2 model through transfer learning. The performance of these models was evaluated using a subset of the Fruits-360 dataset chosen to simulate real-world conditions for small-scale producers. MobileNetV2 was selected for its compact size and efficiency, suitable for devices with limited computational resources. Both approaches achieved high accuracy, with the transfer learning model demonstrating faster convergence and slightly better performance. Feature map visualizations provided insight into the model’s decision-making, highlighting damaged areas of fruits which enhances transparency and trust for end users. This study underscores the potential of deep learning models to modernize fruit quality assessment, offering practical, efficient, and interpretable tools for small-scale farmers.