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.
Highlights
Intelligent equipment with machine vision is a future trend of agricultural machinery [1]
Conducted the maize ear recognition based on deep learning, and their research purpose was to distinguish different maize cultivars through maize ear images; the experimental results showed that the distinguish rate could reach 94.6%
Zhang et al [5] conducted research based on machine vision, which aimed at distinguishing abnormal maize ears. eir research applied the support vector machine (SVM) and back propagation neural network (BPNN), and the experimental results showed that the accuracies were 96, 93.3, and 90% for mildew ears, worm ears, and mechanical damaged ears, respectively
Summary
Intelligent equipment with machine vision is a future trend of agricultural machinery [1]. Conducted the maize ear recognition based on deep learning, and their research purpose was to distinguish different maize cultivars through maize ear images; the experimental results showed that the distinguish rate could reach 94.6%. Similar to Mathematical Problems in Engineering maize ear recognition, researchers applied substantial methods to realize maize plant recognition, such as machine vision, deep learning, and travel switch, and experimental results showed that the deep learning method is more accurate, while the travel switch method is more cost-effective [6, 7]. Conventional machine vision technology needs several characters to extract aiming objects, but it does not show satisfied detection results in complicated background images. Owing to deep learning does not need to depict detail target features, image processing methodology does not rely on high-speed computer hardware, and this study would take both advantages to recognize maize ears. The two algorithms would be combined to have a field experiment
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