AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification

arXiv:2609.10469 · cs.CV · Submitted 2026-09-09 · Read on arXiv

cs.CV

Submitted: 2026-09-09

Updated: 2026-09-20

Code: https://github.com/Abdullah2104/agrovisnet

Terminology

• Accuracy — 107× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Multi-Scale Depthwise Block — 13× in this paper
• Interpretability — 9× in this paper · explained in 15 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Quantization — 5× in this paper · explained in 12 episodes
• Core — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Transfer Learning — 4× in this paper · explained in 10 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Computational Biology — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Enhanced Residual Blocks — 1× in this paper
• Explainability — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Higher-order derivatives — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Wavelet Transform — 1× in this paper · explained in 2 episodes

Sources

• RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Separable Self-attention for Mobile Vision Transformers
• Searching for Activation Functions

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