A Method for Obtaining the Number of Maize Seedlings Based on the Improved YOLOv4 Lightweight Neural Network

Abstract

Obtaining the number of plants is the key to evaluating the effect of maize mechanical sowing, and is also a reference for subsequent statistics on the number of missing seedlings. When the existing model is used for plant number detection, the recognition accuracy is low, the model parameters are large, and the single recognition area is small. This study proposes a method for detecting the number of maize seedlings based on an improved You Only Look Once version 4 (YOLOv4) lightweight neural network. First, the method uses the improved Ghostnet as the model feature extraction network, and successively introduces the attention mechanism and k-means clustering algorithm into the model, thereby improving the detection accuracy of the number of maize seedlings. Second, using depthwise separable convolutions instead of ordinary convolutions makes the network more lightweight. Finally, the multi-scale feature fusion network structure is improved to further reduce the total number of model parameters, pre-training with transfer learning to obtain the optimal model for prediction on the test set. The experimental results show that the harmonic mean, recall rate, average precision and accuracy rate of the model on all test sets are 0.95%, 94.02%, 97.03% and 96.25%, respectively, the model network parameters are 18.793 M, the model size is 71.690 MB, and frames per second (FPS) is 22.92. The research results show that the model has high recognition accuracy, fast recognition speed, and low model complexity, which can provide technical support for corn management at the seedling stage.

Comparison of Drone-Assisted Maize Seedling Detection Models Based on Deep Learning

“…The multiple required complex processing stages render R-CNN models relatively slow, limiting their application potential in large-scale operations. Based on the improvements of YOLOv4, Gao et al proposed a lightweight model for seedling detection with an enhanced feature extraction network, a novel attention mechanism, and a k-means clustering algorithm [14] . Zhang et al further improved the efficacy and speed of maize male cob detection by optimizing the feature extraction network and introducing a multi-head attention mechanism [15] .…”

An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery

“…Banerjee et al [ 17 ] combined spectral and morphological information extracted from UAV multispectral images to effectively estimate wheat emergence using machine learning regression analysis, but the lower resolution posed difficulties in detecting the number of wheat emergences. The improved YOLOv4 proposed by Gao et al [ 18 ] achieved accurate detection of maize numbers; they used depth-separable convolution and improved network structure to make the model more lightweight and reduce the number of model parameters, but the smaller range of acquired features for maize in the images made it less efficient. There will be some variation in the detection accuracy of the model for images acquired during different crop growth periods [ 19 ].…”