Abstract
News – Investigators publish new report on artificial intelligence. According to news reporting out of Alexandria, Egypt, by NewsRx editors, research stated, “The demand for high-quality tomatoes to meet consumer and market standards, combined with large-scale production, has necessitated the development of an inline quality grading. Since manual grading is time-consuming, costly, and requires a substantial amount of labor.” Funders for this research include Egypt-japan University of Science And Technology. The news reporters obtained a quote from the research from Egypt-Japan University of Science and Technology: “This study introduces a novel approach for tomato quality sorting and grading. The method leverages pre-trained convolutional neural networks (CNNs) for feature extraction and traditional machinelearning algorithms for classification (hybrid model). The single-board computer NVIDIA Jetson TX1 was used to create a tomato image dataset. Image preprocessing and fine-tuning techniques were applied to enable deep layers to learn and concentrate on complex and significant features. The extracted features were then classified using traditional machine learning algorithms namely: support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) classifiers. Among the proposed hybrid models, the CNN-SVM method has outperformed other hybrid approaches, attaining an accuracy of 97.50% in the binary classification of tomatoes as healthy or rejected and 96.67% in the multiclass classification of them as ripe, unripe, or rejected when Inceptionv3 was used as feature extractor.”
NETL
NSTL
IEEE