Segmentation and counting of wheat spikes and grains based on texture features and deep learning
The number of grains per ear is an important factor in the composition of wheat yield and one of the parameters for estimating wheat yield.The traditional manual counting method is time-consuming and labor-in-tensive,and human factors have a great influence.In order to realize the intelligent and rapid monitoring of the num-ber of grains per ear,three varieties of Bainong 307,Xin-mai 26 and Jimai 336 were used as test materials,and the wheat ear images were taken with a smart phone at the late stage of wheat grain filling.Based on the image processing tech-nology,the wheat ear images were preprocessed and normalized to 480×480 pixels.Combining deep learning and transfer learning mechanisms,a HRNet wheat spikelet segmentation and counting deep learning model based on the freeze-thaw mechanism was constructed.Image processing algorithms and wheat spikelet texture features were used to determine the threshold of the relationship between the number of spikelet pixels and the number of grains per spike.The spikelet-grain number prediction model was constructed to realize the prediction and counting of wheat spikes.The results showed that compared with PSPNet,DeeplabV3+segmentation model,U-Net which also used freeze-thaw mechanism and HRNet with-out freeze-thaw mechanism,the HRNet model based on the freeze-thaw mechanism had a better segmentation effect on wheat spikelets,and had better robustness.The segmentation accuracy was 0.959 4,the mean intersection over union(mI-oU)was 0.911 9,the mean pixel accuracy(mPA)was 0.941 9,and the recall rate was 0.941 9.The spikelets were coun-ted by the images of three different wheat varieties.The determination coefficient(R2)was 0.92,the average absolute error was 0.73,and the average relative error was 2.89%.The R2 of grain counting was 0.92,the average absolute error was 0.43,and the average relative error was 5.51%.It shows that the HRNet wheat spikelet image segmentation algorithm through the freeze-thaw mechanism can effectively segment wheat spikelets and obtain richer semantic information,which can be used to solve the problems of difficult segmentation of small target images and training underfitting.The model can quickly and accurately predict the number of wheat grains,so as to provide algorithm support for efficient and intelligent yield estimation of wheat.
万方数据
维普
