RGB-Based Deep Learning for Freeze Damage Detection in Strawberry: Comparing Scratch and Transfer Learning Approaches on Custom Data.

Abstract

Freeze damage presents a critical threat to agricultural productivity, resulting in substantial economic losses, especially in sensitive crops such as strawberries. Traditional methods for assessing freeze damage, including manual inspection, are time-consuming, subjective, and labor-intensive. In this study, a deep learning (DL) and computer vision-based approach was proposed to automate freeze damage classification in strawberry plants using RGB images. The performance of four convolutional neural network (CNN) architectures was evaluated: DenseNet-121, Inception V3, ResNet-50, and Xception. Two training methods are compared: transfer learning (TL) using pretrained ImageNet weights and training models from scratch. The models are assessed based on classification accuracy, precision, recall, F1-score, and inference time. The results indicate that models trained from scratch outperform TL models, achieving up to 97% accuracy with ResNet-50, whereas TL models attained a maximum accuracy of 84%. The ResNet-50 model also achieved the fastest inference time (3.0 s) while DenseNet-121 was the smallest (26. 86 MB). Furthermore, the models were most effective at identifying severely damaged plants but struggled to differentiate mild damage from minimal or no damage. The findings suggest that scratch-trained models deliver more accurate solutions for freeze damage classification in strawberry plants. Additionally, DenseNet-121 was the best choice for memory-limited applications, while ResNet-50 excelled in speed-sensitive tasks. This study underscores the potential of deep learning and computer vision to automate freeze damage assessment in strawberry plants, providing a more accurate, rapid, and nondestructive alternative to traditional methods.