AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Medicinal plants are integral to both traditional healing practices and modern pharmacological research. However, accurate identification of these plants remains a persistent challenge, particularly for individuals without botanical expertise. This paper presents a deep learning-based system for automatic medicinal pla...
Medicinal plant leaves are central to traditional healthcare in biodiversity-rich countries such as Indonesia, yet high visual similarity between species makes manual identification error-prone. Classification methods for medicinal and herbal leaves have shifted from hand-crafted feature classifiers to deep convolution...
This study applies transfer learning to two CNN architectures, MobileNetV2 and ResNet50, to classify red onion leaf diseases into three classes yellow spot, leaf curl, and healthy leaves, and shows that ResNet50 demonstrates better generalization, with a stable training curve, and outperforms MobileNetV2 in terms of ac...
A fine-tuned InceptionV3 convolutional neural network to classify five groundnut leaf classes showed excellent performance, especially for minority classes like rosette and rust, which support its use in real-world disease diagnosis in agriculture.
The identification of medicinal plants through leaf characteristics was considered an important approach for supporting the utilization of herbal plants, particularly those associated with hypertension management. However, similarities in visual characteristics among plant species and variations in image conditions pre...
Plant diseases significantly threaten global food security, necessitating accurate and automated diagnostic systems. This work presents a modular deep learning framework for systematic benchmarking and comparative analysis of multiple architectures using the PlantVillage dataset. The framework integrates pretrained m...
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