## 5 Conclusions  
The IBSA_Net proposed in this study combines the inverted bottleneck network and Shuffle Attention mechanism while also incorporating the HardSwish activation function, IBMax, and other modules. By doing so, the proposed network achieves not only superior recognition accuracy but also a faster convergence speed. In addition, it can extract multi-scale plant disease spot features, accurately locate disease areas, and identify tomato leaf diseases with fine granularity. To address the issue of the limited generalization ability of models trained on a single background dataset in natural agricultural production environments, we propose a transfer learning-based training method for small sample datasets. Specifically, we utilize the PDDA dataset, which simulates a complex natural background, to pre-train the model clusters. Experimental results demonstrate that the pre-trained IBSA_Net on PDDA achieves the best performance on the real dataset. The mean values of 0.942, 0.944, and 0.943 for the precision, recall, and F1-score, respectively, highlight the effectiveness of our approach. In addition, the results of attention visualization experiments demonstrated that the PDDA model group exhibited a significantly larger warm-colored area overlapping with the lesions compared to the PlantVillage model group. Moreover, the IBSA_Net disease recognition area demonstrated the highest overlap with the entire lesion area of the leaf while also exhibiting a good ability to differentiate between \"false lesions\" and real lesion areas. Finally, we tested the performance of IBSA_Net on other crops and achieved good results. These findings suggest that our model and methodology offer an effective and reliable approach to identifying leaf diseases in complex backgrounds, with potential applicability to other crops. Nonetheless, this study has certain limitations. Due to the challenges associated with collecting tomato leaf datasets, there is a paucity of tomato leaf samples exhibiting growth defects caused by nutritional deficiencies. Furthermore, in actual agricultural production, some symptoms of viral diseases are akin to those caused by nutritional deficiencies, leading to network misjudgment and the incorrect identification of nutrient-deficient leaves as disease-afflicted. In future work, we will gather additional tomato leaf samples with growth defects resulting from different causes and refine the model proposed herein to enable better differentiation of these variations. Conceptualization, R.Z. and Y.W. ; methodology, R.Z. and Y.W. ; software, J.P.; validation, R.Z., H.C. and J.P.; data curation, H.C.; writing--original draft preparation, R.Z. ; writing--review and editing, Y.W. ; visualization, P.J. ; funding acquisition and supervision, Y.W. and P.J. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline AlexNet & 0.795 & 0.807 & 0.811 & 0.809 \\ VGG19 & 0.871 & 0.887 & 0.886 & 0.886 \\ GoogLeNet & 0.917 & 0.933 & 0.918 & 0.925 \\ MobileNetV3 & 0.946 & 0.946 & 0.941 & 0.943 \\ EfficientNet & 0.981 & 0.978 & 0.980 & 0.979 \\ IBSA\_Net & 0.992 & 0.995 & 0.993 & 0.994 \\ \hline \end{tabular}
\end{table} 
Table 10: Performance of the six models on the pepper leaf disease dataset. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline AlexNet & 0.635 & 0.613 & 0.634 & 0.623 \\ VGG19 & 0.761 & 0.732 & 0.760 & 0.746 \\ GoogLeNet & 0.652 & 0.776 & 0.681 & 0.725 \\ MobileNetV3 & 0.835 & 0.839 & 0.835 & 0.837 \\ EfficientNet & 0.935 & 0.934 & 0.925 & 0.929 \\ IBSA\_Net & 0.928 & 0.945 & 0.931 & 0.938 \\ \hline \end{tabular}
\end{table} 
Table 9: Performance of the six models on the apple leaf data set. **Funding:** This research was funded by the National Key R&D Program of China under the sub-project \"Research and System Development of Navigation Technology for Harvesting Machine of Special Economic Crops\" (No.