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Accurate counting of wheat ears in field conditions is vital to predict yield and for crop breeding. To quickly and accurately obtain the number of wheat ears in a field, we propose herein a method to count wheat ears based on fully convolutional network (FCN) and Harris corner detection. The technical procedure consists essentially of 1) constructing a dataset of wheat-ear images from acquired red-green-blue (RGB) images; 2) training a FCN as the wheat-ear segmentation model by using the constructed image dataset; 3) preparing testing images and inputting them into the segmentation model to get the initial segmentation results; 4) binarizing the initial segmentation by using the Otsu algorithm (to facilitate subsequent processing); and 5) applying Harris corner detection after extracting the wheat-ear skeleton to obtain the number of wheat ears in the images. The segmentation results show that the proposed FCN-based segmentation model segments wheat ears with an average accuracy of 0.984 and at low computational cost. An average of only 0.033 s is required to segment a 256× 256 -pixel wheat-ear image. Moreover, the segmentation result is improved by nearly 10% compared with the previous segmentation methods under conditions of wheat-ear occlusion, leaf occlusion, uneven illumination, and soil disturbance. Subsequently, the proposed counting method achieves good results, with an average accuracy of 0.974, a coefficient of determination (R2) of 0.983, and a root mean square error (RMSE) of 14.043. These metrics are all improved by 10% compared with the previous methods. These results show that the proposed method accurately counts wheat ears even under conditions of wheat-ear adhesion. Furthermore, the results provide an important technique for studying wheat phenotyping.

Wheat (Triticum aestivum L.) is one of the three major cereals worldwide. The FusaHum graminearum Sehw., special fugus always damages the wheat ear, and produces vomitoxin,is difficult to control and prevent, and seriously threatens the health of humans, animals and China's food security. Currently, rapidly, accurately and non-destructively diagnostic devices or systems for this disease have not been disclosed. In this study, the infected ears with different severities were picked up in key growth stages. The diseased area of wheat ear was extracted using hypergreen characteristic, and a total of 30 features of infected ears were chosen including color (Lab, HSI, HSV, YCbCr color space), texture (LBP and LLE dimension reduction), and shape (squareness, shape complexity, and eccentricity). Then using the competitive adaptive re-weighted sampling (CARS) and rough set algorithm (RS) to screen the characteristics of the diseased ear, the four characteristics with the largest contribution were determined to establish the CARS-SVM and CARS-RS-SVM models respectively. The study found that the recognition rate of CARS-SVM model is 85.4%, while CARS-RS-SVM model is 92.7%. Thus the CARS-RS-SVM was thought of as the optimal model by two indicators of identification accuracy. On the basis, a wheat scab diagnosis system based on Android mobile phone was constructed. It consists of three parts - Clients, Service-Terminal and Database. The Client was designed by Android Studio and its functions mainly include image acquisition, image storage, GPS positioning, image uploading and diagnostic results display. The Service-Terminal was completed by the mixed programming of Myeclipse and Matlab software, and Tomcat was used as the Server. It mainly implements the functions of image receiving, image preprocessing, feature extraction and selection, and classifier modeling. The MySQL was used to establish two databases: the “Disease Characteristics Database” and the “Disease Diagnosis Knowledge Base”. Finally, through samples testing and validating, the Android-based mobile terminal can real-time collect the image of Fusarium head blight and upload the server. After the target image was processed and compared by the “Disease Characteristics Database”, the appropriate diagnostic knowledge was selected from the “Disease Diagnosis Knowledge Base” and feedbacked to the client. In summary, the results of this study showed that it was helpful for the rapid and non-destructive investigation of infected FHB in the field, and it would provide a reference for the study of other crop diseases, facilitate the application and development of new technologies such as artificial intelligence and big data in agriculture.

When large-scale centralized grid connection, it will affect the safe and stable operation of the power grid. In order to ensure the safe and stable operation of power grid after wind power grid integration, it is necessary to study the penetration rate of wind power. In this paper, the grid structure is introduced into the wind power installation layout analysis to study the penetration limit of the regional power grid. The research shows that the strength of wind power grid is the decisive condition for the stable operation of the wind power grid system.