Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers - Li, Xiaopeng; Li, Shuqin (Agriculture (Basel), 2022)
Keywords: 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