## 1 Introduction
(2020) used pre-trained CNN architectures (such as Inception V3, VGG16, VGG19 and ResNet) to detect tuna absoluta virus infection in tomato leaves, finding that Inception V3 had a detection accuracy of 87.20% better than other architectures [20]. [PERSON] used a hybrid deep learning approach to detect tomato plant diseases from leaf images, integrating pre-trained EfficientNetB3 and MobileNet models to achieve 99.92% accuracy [21]. [PERSON], [PERSON] developed an Android system for diagnosing ginger plant diseases by training VGG and MobileNetV2 models on a real dataset of ginger leaf images, achieving real-time detection with high performance [22]. [PERSON] proposed two CNN models for tomato leaf disease detection, optimized via fine-tuning, particle swarm optimization, and grid search. Their ensemble model achieved 99.60% accuracy with fast training and testing times [23]. [PERSON] created \"LiteCNN,\" a lightweight neural network that achieved 95.24% accuracy through knowledge distillation. The network was then deployed on an FPGA for a low-power, high-precision, and fast plant disease recognition terminal, suitable for real-time recognition in the field [24]. [PERSON] proposed a transfer learning-based deep feature descriptor that combined deep and hand-crafted features through feature fusion to capture local texture information in plant leaf images. The approach achieved accuracies of 99.79%, 92.59%, and 97.12% on three different datasets [25]. [PERSON] proposed a mobile lightweight deep learning model that achieved accuracies of 97%, 97.1%, and 96.4% on apple, citrus, and tomato leaf datasets, respectively. The model had approximately 88 million multiply-accumulate operations, 260,000 parameters, and 1 MB of storage space [26]. Image-based methods for tomato leaf disease recognition have limitations. They require large-scale annotated datasets for training, which are difficult to obtain and may lack representativeness. Additionally, they do not fully consider the diversity and complexity of image acquisition conditions in real environments, such as illumination, angle, occlusion, noise, and other factors. Transfer learning techniques are also not effectively utilized to improve the model's generalization and adaptation abilities on different datasets. To overcome these limitations, this paper proposes a novel convolutional neural network model, IBSA_Net, which combines the inverted bottleneck structure and Shuffle Attention mechanism. The paper also explores the impact of activation functions, such HardSwish, and MaxPool layer, on the network's performance. The proposed model achieves high accuracy, precision, recall, and F1-score on tomato leaf disease classification tasks and quickly identifies tomato leaf disease regions in real environments. Furthermore, this paper verifies the validity of IBSA_Net on other crops. The main contributions of this paper are as follows:  
* The IBMax module is proposed to enhance model recognition accuracy by downsizing feature maps and expanding the perceptual field. * The Shuffle Attention mechanism is introduced and alternately embedded into the network modules to enable the model to focus accurately on tomato leaf disease regions. * The PlantDoc++ dataset is constructed, and an innovative transfer learning approach is utilized to address the problem of models trained on a single background dataset being unable to generalize to real-world agricultural production environments.