Search Results (2)

This study presents a robust and extensible hybrid classification framework for accurately detecting diseases in citrus leaves by integrating transfer learning-based deep learning models with classical machine learning techniques. Features were extracted using advanced pretrained architectures—DenseNet201, ResNet50, MobileNetV2, and EfficientNet-B0—and refined via the minimum redundancy maximum relevance (mRMR) method to reduce redundancy while maximizing discriminative power. These features were classified using support vector machines (SVMs), ensemble bagged trees, k-nearest neighbors (kNNs), and neural networks under stratified 10-fold cross-validation. On the lemon dataset, the best configuration (DenseNet201 + SVM) achieved 94.1 ± 4.9% accuracy, 93.2 ± 5.7% F1 score, and a balanced accuracy of 93.4 ± 6.0%, demonstrating strong and stable performance. To assess external generalization, the same pipeline was applied to mango and pomegranate leaves, achieving 100.0 ± 0.0% and 98.7 ± 1.5% accuracy, respectively—confirming the model’s robustness across citrus and non-citrus domains. Beyond accuracy, lightweight models such as EfficientNet-B0 and MobileNetV2 provided significantly higher throughput and lower latency, underscoring their suitability for real-time agricultural applications. These findings highlight the importance of combining deep representations with efficient classical classifiers for precision agriculture, offering both high diagnostic accuracy and practical deployability in field conditions. Full article

In this work, magnetite nanoparticles (Fe₃O₄) that are well dispersed by a submicron sized carbon framework in a pomegranate shape are engineered using a flexible one-step spray pyrolysis strategy. Under inert gas atmosphere, the homogeneously mixed Fe³⁺ ions and chitosan (CS) molecules are in situ transformed to Fe₃O₄ nanoparticles and spherical nitrogen-doped carbon coating domains, respectively. Moreover, the obtained Fe₃O₄@C composite exhibits a unique submicron sized pomegranate configuration, in which favorable electric/ionic pathways have been constructed and the Fe₃O₄ nanoparticles have been effectively dispersed. When used as an anode electrochemical active material, the Fe₃O₄@C composite exhibits impressive lithium-ion storage capabilities, and maintains a reversible capacity of 500.2 mAh·g⁻¹ after 500 cycles at a high current density of 1000 mA·g⁻¹ as well as good rate capability. The strategy in this work is straightforward and effective, and the synthesized Fe₃O₄@C material has good potential in wider applications. Full article