Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers

• Published in: Agriculture (Basel) Vol. 12; no. 6; p. 884
• Main Authors: Li, Xiaopeng, Li, Shuqin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Accuracy; agriculture; Apples; Artificial intelligence; complex environments; Complexity; Crop diseases; Crops; data collection; Datasets; Embedding; Experiments; Feature extraction; Floating point arithmetic; foliar diseases; Fruits; hybrid model; hybrids; Identification; identification of apple diseases; image classification; Image contrast; information exchange; Leaves; Lightweight; lightweight model; Mathematical models; Medical imaging; Methods; Neural networks; Parameters; Plant diseases; Support vector machines; Vision Transformer

The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transfo...