Tness of the MAF module proposed in this paper, we also used the data set

Tness of the MAF module proposed in this paper, we also used the data set collected in the Science Park in the west campus of China Agriculture University, like the photos of maize ailments such as southern leaf blight, fusarium head blight, and these three kinds talked about above. Furthermore, we developed the mobile detection device depending on the iOS platform, which won the second prize inside the National Laptop or computer Design Competition for Chinese College Students. As shown in Figure 20, the optimized model depending on the proposed process can speedily and successfully detect maize illnesses in practical application scenarios, proving the proposed model’s robustness.Figure 20. Screenshot of launch page and detection pages.five. Conclusions This paper proposed an MAF module to optimize mainstream CNNs and gained great final results in detecting maize leaf diseases with the accuracy reaching 97.41 on MAF-ResNet50. Compared with the original network model, the accuracy improved by 2.33 . Because the CNN was unstable, AZD4635 Biological Activity non-convergent and overfitting when the image set was insufficient, many image pre-processing solutions, meanwhile, models had been applied to extend and augment the information of illness samples, such as DCGAN. Transfer learning and warm-up methods have been adopted to accelerate the coaching speed of the model. To confirm the effectiveness from the proposed technique, this paper applied this model to various mainstream CNNs; the results indicated that the performance of networks addingRemote Sens. 2021, 13,18 ofthe MAF module have all been improved. Afterward, this paper discussed the overall performance of distinct combinations of five base activation functions. Based on a big quantity of experiments, the combination of Sigmoid, ReLU (or tanh), and Mish (or LeakReLU) reached the highest price of accuracy, which was 97.41 . The outcome proved the effectiveness of the MAF module, along with the improvement is of considerable significance to agricultural production. The optimized module proposed in this paper is often properly applied to several CNNs. In the future, the author will make efforts to replace the combination of linear activation functions with that of nonlinear activation functions and make extra network parameters participate in model coaching.Author Contributions: Conceptualization, Y.Z.; methodology, Y.Z.; validation, Y.Z., X.Z.; writing– original draft preparation, Y.Z.; writing–review and editing, Y.Z., S.W.; visualization, Y.L., P.S.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Q.M. All authors have study and agreed to the published version on the manuscript. Funding: This perform was supported by the 2021 All-natural Science Fund Project in Shandong Province (ZR202102220347). Institutional Critique Board Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: Not applicable. Acknowledgments: We are AZD1208 Autophagy grateful towards the ECC of CIEE in China Agricultural University for their robust help during our thesis writing. We’re also grateful for the emotional assistance offered by Manzhou Li to the author Y.Z. Conflicts of Interest: The authors declare no conflict of interest.
remote sensingArticleContinuous Detection of Surface-Mining Footprint in Copper Mine Employing Google Earth EngineMaoxin Zhang 1 , Tingting He 1, , Guangyu Li two , Wu Xiao 1 , Haipeng Song 1 , Debin Luand Cifang WuDepartment of Land Management, Zhejiang University, Hangzhou 310058, China; [email protected] (M.Z.); [email protected] (W.X.); sh.