Dough-Stage Maize (Zea mays L.) Ear Recognition Based on Multiscale Hierarchical Features and Multifeature Fusion

Abstract

Crop-related object recognition is of great importance in realizing intelligent agricultural machinery. Maize (Zea mays. L.) ear recognition could be a representative of crop-related object recognition, which is a critical technological premise for realizing automatic maize ear picking and maize yield prediction. In order to recognize maize ears in dough stage, this study combined deep learning and image processing, which have advantages of feature extraction and hardware flexibility, respectively. LabelImage was applied to mark and label maize plants, based on the deep learning framework TensorFlow, and this study developed multiscale hierarchical feature extraction together with quadruple-expanded convolutional kernels. To recognize the whole maize plant, 1250 images were acquired for training the recognition model, and its performance in a test set showed that the recognition accuracy was 99.47%. Subsequently, multifeatures of maize ear were determined, and the optimum binary threshold was obtained by fitting Gaussian distribution in the subblock image. Consequently, the maize ear was recognized by morphological process which was conducted by Python and OpenCV. Experiment was conducted in August 2018, and 10800 images were acquired for testing this algorithm. Experimental results showed that the average recognition accuracy was 97.02% and time consumption was 0.39 s for each image, which could meet a forward speed of 4.61 km/h for combine harvesters.