AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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UniDetNet-MAF is presented, a lightweight unified detection framework that simultaneously localizes and classifies sugarcane leaf diseases and is suitable for timely, field-scale sugarcane disease diagnosis.
UniDetNet-MAF is presented, a lightweight unified detection framework that simultaneously localizes and classifies sugarcane leaf diseases and is suitable for timely, field-scale sugarcane disease diagnosis.
Accurate and robust detection of pear leaf diseases and pests is important for early warning in orchards, precision control, and intelligent disease and pest management. In natural environments, this detection task is still difficult because target scales vary greatly, lesion boundaries are often blurred, disease sympt...
Tomato crop yield can be enhanced by using advanced agricultural technologies when plant leaf diseases are detected early. This article proposes a new tomato disease classification model, called Multiscale Parallel Feature Aggregation Network with Attention Fusion (MPFAN-AF), that classifies diseases from leaf images....
The Wombat Escape Strategy-Hippopotamus Optimization Algorithm-based Recalibrated Multi-Scale Squeeze and Excitation Convolutional Neural Network (WES-HOA-based ReScaleX-CNN) is proposed to classify plant leaf disease accurately.
Global citrus production is significantly impacted by diseases, leading to economic losses and requiring accurate diagnostic tools. The system proposed here presents a deep learning system leveraging the EfficientNetB3 architecture for automated detection of Citrus Greening (HLB), Black Spot, Canker, and healthy instan...
A unique, computationally efficient triple-feature block network capable of highly accurate plant disease classification across diverse species and complex imaging environments is proposed.
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