Manual mango variety classification is time-consuming, error-prone, and contributes significantly to post-harvest losses in developing economies. This study aims to develop a computationally efficient and highly accurate artificial intelligence framework for automated mango variety classification suitable for real-time applications. Eight deep transfer learning models were evaluated as feature extractors and combined with ten classical machine-learning classifiers. Model performance was assessed using accuracy, log loss, memory usage, training time, and inference latency. The hybrid models EfficientNetB0–Linear Discriminant Analysis (LDA) and ResNet50–Logistic Regression achieved 100% test accuracy while reducing inference time by up to 330 times compared to full Convolutional Neural Network (CNN) models. These findings demonstrate that hybrid deep-learning and machine-learning architectures can deliver state-of-the-art accuracy with substantially lower computational cost. Future research will focus on large-scale real-world validation and embedded hardware deployment for industrial fruit sorting systems.
Meat quality and safety are critical aspects of global food security. However, traditional evaluation techniques, including sensory analysis and chemical and instrumental tests, are constrained by subjectivity, high time consumption, their destructive character, and susceptibilit…
Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varie…
Histamine (HIS) in food products can cause poisoning or intolerance reactions, which are usually associated with fish and seafood consumption. Therefore, the detection and quantification of histamine in fish and fish products are important for public health protection. This artic…
This study aimed to develop shrimp gravlax (Penaeus japonicus) as a ready-to-eat seafood product and to determine its shelf life. The product was prepared using a curing method and stored at 4 °C for 30 days. Quality changes were monitored at five-day intervals through analyses o…
This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources—coriander…
Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A…