AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A lightweight deep learning-based approach for automatic detection and classification of walnut leaf diseases using convolutional neural networks (CNNs) is proposed, demonstrating that the proposed CNN model significantly outperforms conventional machine learning algorithms and pre-trained deep learning models.
Background: Groundnut is a vital crop affected by several foliar diseases, such as leaf spot, alternaria, rust and rosette. These diseases can reduce crop quality and yield. Manual identification is time-consuming and may lack accuracy. Deep learning methods offer a reliable alternative for automated disease detection....
Early and accurate detection of plant diseases is critical in precision agriculture to improve crop management and yield. Mungbean (Vigna radiata L.) is highly susceptible to several foliar diseases, including yellow mosaic, powdery mildew, leaf crinkle, and cercospora leaf spot, which cause substantial productivity lo...
This research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations to strengthen plant disease monitoring systems.
A deep learning-based solution to automate disease detection of groundnut leaf conditions that outperformed existing methods such as ResNet50, CNN with progressive resizing, LeafNet and LeafNet and maintained low training and validation loss throughout training.
Findings indicate that the lightweight MobileNet architecture can effectively support reliable and scalable faba bean disease detection under real-world agricultural conditions.
Background: Accurate and timely diagnosis of foliar diseases is the most crucial factor in efforts to maximize crop yield and ensure sustainability. Existing deep learning models, especially single-backbone CNNs, have achieved promising results; however, they often fail to generalize well in different orchard condition...
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