A Deep Transfer Learning Approach for Accurate Dragon Fruit Ripeness Classification and Visual Explanation using Grad-CAM

Abstract

Dragon fruit, known for its rich antioxidant content and low-calorie attributes, has garnered significant attention as a health-promoting fruit. Its economic value has also surged due to increasing consumer demand and its potential as an export commodity in various regions. The classification of dragon fruit ripeness is a pivotal task in ensuring product quality and minimizing post-harvest losses. This research article presents a comprehensive study on the classification of ripe and unripe dragon fruits (Hylocereus spp) using the Densenet201 model through three distinct approaches: as a classifier, feature extrac-tor, and fine-tuner. To explain the outcomes of the image clas-sification model and thereby enhance its performance, optimiza-tion, and reliability, this study employs advanced visualization techniques. Specifically, it utilizes Grad-CAM (Gradient-weighted Class Activation Mapping) and Guided Grad-CAM techniques. These techniques offer insights into the model’s decision-making process and pinpoint regions of interest within the images. This approach empowers researchers to iteratively validate the model’s accuracy and enhance its performance. The utilization of Densenet201 as a classifier, feature extractor, and fine-tuner, coupled with the insights from Grad-Cam and Guided Grad-Cam, presents a holistic approach to enhancing dragon fruit ripeness classification. The findings contribute to the broader discourse on agricultural technology, image analysis, and the optimization of classification models.

Topics & Concepts