Wheat Lodging Types Detection Based on UAV Image Using Improved EfficientNetV2

Abstract

ObjectiveWheat, as one of the major global food crops, plays a key role in food production and food supply. Different influencing factors can lead to different types of wheat lodging, e.g., root lodging may be due to improper use of fertilizers. While stem lodging is mostly due to harsh environments, different types of wheat lodging can have different impacts on yield and quality. The aim of this study was to categorize the types of wheat lodging by unmanned aerial vehicle (UAV) image detection and to investigate the effect of UAV flight altitude on the classification performance.MethodsThree UAV flight altitudes (15, 45, and 91 m) were set to acquire images of wheat test fields. The main research methods contained three parts: an automatic segmentation algorithm, wheat classification model selection, and an improved classification model based on EfficientNetV2-C. In the first part, the automatic segmentation algorithm was used to segment the UAV to acquire the wheat test field at three different heights and made it into the training dataset needed for the classification model. The main steps were first to preprocess the original wheat test field images acquired by the UAV through scaling, skew correction, and other methods to save computation time and improve segmentation accuracy. Subsequently, the pre-processed image information was analyzed, and the green part of the image was extracted using the super green algorithm, which was binarized and combined with the edge contour extraction algorithm to remove the redundant part of the image to extract the region of interest, so that the image was segmented for the first time. Finally, the idea of accumulating pixels to find sudden value added was used to find the segmentation coordinates of two different sizes of wheat test field in the image, and the region of interest of the wheat test field was segmented into a long rectangle and a short rectangle test field twice, so as to obtain the structural parameters of different sizes of wheat test field and then to generate the dataset of different heights. In the second part, four machine learning classification models of support vector machine (SVM), K nearest neighbor (KNN), decision tree (DT), and naive bayes (NB), and two deep learning classification models (ResNet101 and EfficientNetV2) were selected. Under the unimproved condition, six classification models were utilized to classify the images collected from three UAVs at different flight altitudes, respectively, and the optimal classification model was selected for improvement. In the third part, an improved model, EfficientNetV2-C, with EfficientNetV2 as the base model, was proposed to classify and recognized the lodging type of wheat in test field images. The main improvement points were attention mechanism improvement and loss function improvement. The attention mechanism was to replace the original model squeeze and excitation (SE) with coordinate attention (CA), which was able to embed the position information into the channel attention, aggregate the features along the width and height directions, respectively, during feature extraction, and capture the long-distance correlation in the width direction while retaining the long-distance correlation in the length direction, accurate location information, enhancing the feature extraction capability of the network in space. The loss function was replaced by class-balanced focal loss (CB-Focal Loss), which could assign different loss weights according to the number of valid samples in each class when targeting unbalanced datasets, effectively solving the impact of data imbalance on the classification accuracy of the model.Results and DiscussionsFour machine learning classification results: SVM average classification accuracy was 81.95%, DT average classification accuracy was 79.56%, KNN average classification accuracy was 59.32%, and NB average classification accuracy was 59.48%. The average classification accuracy of the two deep learning models, ResNet101 and EfficientNetV2, was 78.04%, and the average classification accuracy of ResNet101 was 81.61%. Comparing the above six classification models, the EfficientNetV2 classification model performed optimally at all heights. And the improved EfficientNetV2-C had an average accuracy of 90.59%, which was 8.98% higher compared to the average accuracy of EfficientNetV2. The SVM classification accuracies of UAVs at three flight altitudes of 15, 45, and 91 m were 81.33%, 83.57%, and 81.00%, respectively, in which the accuracy was the highest when the altitude was 45 m, and the classification results of the SVM model values were similar to each other, which indicated that the imbalance of the input data categories would not affect the model's classification effect, and the SVM classification model was able to solve the problem of high dimensionality of the data efficiently and had a good performance for small and medium-sized data sets. The SVM classification model could effectively solve the problem of the high dimensionality of data and had a better classification effect on small and medium-sized datasets. For the deep learning classification model, however, as the flight altitude increases from 15 to 91 m, the classification performance of the deep learning model decreased due to the loss of image feature information. Among them, the classification accuracy of ResNet101 decreased from 81.57% to 78.04%, the classification accuracy of EfficientNetV2 decreased from 84.40% to 81.61%, and the classification accuracy of EfficientNetV2-C decreased from 97.65% to 90.59%. The classification accuracy of EfficientNetV2-C at each of the three altitudes. The difference between the values of precision, recall, and F1-Score results of classification was small, which indicated that the improved model in this study could effectively solve the problems of unbalanced model classification results and poor classification effect caused by data imbalance.ConclusionsThe improved EfficientNetV2-C achieved high accuracy in wheat lodging type detection, which provides a new solution for wheat lodging early warning and crop management and is of great significance for improving wheat production efficiency and sustainable agricultural development.

Keywords

• wheat lodging types
• image processing
• deep learning
• unbalanced data
• machine learning
• uav