Abstract:
Objective To accurately, efficiently and non-destructively identify the weeds in rice field using deep convolutional neural network, obtain the optimal network model, and provide a theoretical basis for rice field planting management and variable drone spraying.
Method The weeds in rice field were taken as the main research object, and weed image samples were collected by CCD photosensitive camera to construct weed data set (PFMW) in rice field. The deep convolutional neural network with multiple structures was used to automatically extract the features of the PFMW data set, and then to model and test.
Result VGG16 model achieved the highest precision among all the deep learning models, the F values in Bidens, Goose Starwort, Gomphrena, Sprangle, Eclipta, Wedelia were 0.957, 0.931, 0.955, 0.955, 0.923 and 0.992 respectively, and the average F value was 0.954. The VGG16-SGD model achieved the highest precision in setted deep model optimizer experiments, the F values in each weed mentioned above were 0.987, 0.974, 0.965, 0.967, 0.989 and 0.982 respectively, and the average F value was 0.977. In the equilibrium experiments of sample category quantity in the dataset, the accuracy of the VGG16 model trained by the balanced weed dataset was 0.900, while those of the models trained by the 16.7%, 33.3% and 66.6% category imbalance dataset were 0.888, 0.866 and 0.845 respectively.
Conclusion The machine vision and other advanced technologies can accurately identify weeds in rice field. It is of great significance for promoting fine cultivation of rice field and variable drone spraying, etc., and the technology can effectively assist weed control in the process of agricultural planting.
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