## 5 Conclusions  
In this paper, we extended Deep CORAL, a simple, yet effective end-to-end adaptation in deep neural networks, with an attention mechanism to provide more information for deep neural networks. Meanwhile, we used the p-norm loss function to replace CORAL loss to balance the offset. Experiments on standard benchmark datasets (Office-31) showed state-of-the-art performance. We tested our method on the classic benchmark dataset Office-31, and the experimental results showed us its effectiveness. One of the future research directions is the application of our method to a more diverse range of real-world applications and datasets. In addition, we are performing research on image recognition of vegetable diseases and insect pests under a greenhouse environment, which is very complicated. Different diseases and insect pests overlap, and light changes in real time. We hope to improve the accuracy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \(A\to W\) & \(A\to D\) & \(W\to A\) & \(W\to D\) & \(D\to A\) & \(D\to W\) \\ \hline No Adaptation & \(45.3\pm 0.7\) & \(23.4\pm 1.6\) & \(38.0\pm 0.2\) & \(47.4\pm 1.6\) & \(23.0\pm 0.4\) & \(59.1\pm 0.9\) \\ DDC & \(44.3\pm 0.7\) & \(24.0\pm 1.5\) & \(38.6\pm 0.5\) & \(44.6\pm 1.0\) & \(24.0\pm 0.7\) & \(62.5\pm 0.5\) \\ CDAN & \(42.9\pm 0.9\) & \(35.2\pm 1.8\) & \(34.8\pm 2.4\) & \(73.3\pm 0.7\) & \(37.1\pm 0.4\) & \(83.3\pm 1.3\) \\ CDAN+E & \(43.9\pm 0.9\) & \(36.4\pm 2.4\) & \(36.2\pm 0.7\) & \(71.6\pm 1.3\) & \(34.4\pm 0.5\) & \(78.1\pm 1.9\) \\ Deep CORAL & \(54.77\pm 0.68\) & \(44.14\pm 1.97\) & \(40.19\pm 0.11\) & \(74.74\pm 0.44\) & \(40.9\pm 0.12\) & \(87.62\pm 0.58\) \\ Deep CORAL (\(l=L_{\mathrm{C}}+L_{\mathrm{4}}\)) & \(51.74\pm 0.55\) & \(41.26\pm 1.90\) & \(41.03\pm 0.06\) & \(75.22\pm 0.52\) & \(39.25\pm 0.2\) & \(90.69\pm 0.25\) \\ Deep CORAL+Att & \(55.13\pm 0.29\) & \(48.16\pm 1.29\) & \(41.72\pm 0.21\) & \(74.88\pm 0.58\) & \(40.43\pm 0.11\) & \(89.26\pm 0.20\) \\ P-ADC\({}_{(2-3)}\) & \(55.45\pm 0.24\) & \(\mathbf{49.32\pm 1.34}\) & \(\mathbf{41.89\pm 0.24}\) & \(\mathbf{75.70\pm 0.40}\) & \(41.82\pm 0.07\) & \(91.38\pm 0.20\) \\ P-ADC\({}_{(2-4)}\) & \(\mathbf{56.88\pm 0.67}\) & \(49.02\pm 1.05\) & \(41.01\pm 0.21\) & \(74.62\pm 0.52\) & \(\mathbf{41.93\pm 0.30}\) & \(\mathbf{91.45\pm 0.28}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Target accuracies for all six domain shifts in the Office-31 dataset (training epoch = 100). Note that \(L_{(a-b)}=\sum_{p=a}^{b}\sum_{i=1}^{t}\lambda_{i}l_{p-norm}\), \(L_{\mathrm{C}}=l_{CLASS}\), and \(\text{P-ADC}_{(a-b)}=\text{P-ADC}\Big{(}l_{TOTAL}=L_{\mathrm{C}}+L_{(a-b)} \Big{)}\), where \(l_{CLASS}\) is the classification loss function, \(L_{(a-b)}\) denotes the p-norm loss function where p ranges from a to b, and \(a\) and \(b\) are natural numbers greater than 1. Bold denotes the highest accuracy. Figure 4: Comparative analysis of Deep CORAL and P-ADC\({}_{(2-4)}\) in shift \(D\to W\). (**a**) Training and test accuracies on Deep CORAL and P-ADC\({}_{(2-4)}\). P-ADC\({}_{(2-4)}\) significantly outperforms Deep CORAL on the target domain under the same environmental setting. (**b**) Average loss value of Deep CORAL and P-ADC\({}_{(2-4)}\) in the training and test stage. (**c**) Classification loss value, p-norm loss value, and total loss value of P-ADC\({}_{(2-4)}\), respectively. of vegetable disease and insect pest identification with a domain adaptation method, including this method. Conceptualization, Z.-Y.W. ; methodology, Z.-Y.W. ; software, Z.-Y.W. ; validation, Z.-Y.W. ; formal analysis, Z.-Y.W. ; investigation, Z.-Y.W. ; resources, D.-K.K. ; data curation, Z.-Y.W. ; writing--original draft preparation, Z.-Y.W. ; writing--review and editing, D.-K.K. ; visualization, Z.-Y.W. ; supervision, D.-K.K. ; project administration, D.-K.K. ; funding acquisition, D.-K.K. All authors read and agreed to the published version of the manuscript.