ECVNet: A Fusion Network of Efficient Convolutional Neural Networks and Visual Transformers for Tomato Leaf Disease Identification

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 2985 - 2985

Published: Dec. 15, 2024

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification timely management of these issues. Existing models for tomato disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global recognition; conversely, VTs are advantageous extraction but less effective at capturing features. This discrepancy hampers performance improvement both model types task identification. Currently, fusion that combine still relatively scarce. We developed an efficient network named ECVNet recognition. Specifically, we first designed Channel Attention Residual module (CAR module) focus on channel features enhance model’s sensitivity importance channels. Next, created Fusion (CAF effectively extract integrate features, thereby improving spatial capabilities. conducted extensive experiments using Plant Village dataset AI Challenger 2018 dataset, achieving state-of-the-art cases. Under condition 100 epochs, achieved accuracy 98.88% 86.04% dataset. The introduction provides solution diseases.

Language: Английский

The gene function prediction challenge: Large language models and knowledge graphs to the rescue

Current Opinion in Plant Biology, Journal Year: 2024, Volume and Issue: 82, P. 102665 - 102665

Published: Nov. 22, 2024

Language: Английский