Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

Automatic grading evaluation of winter wheat lodging based on deep learning

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Artificial intelligence method based on multi-feature fusion for automatic macular edema (ME) classification on spectral-domain optical coherence tomography (SD-OCT) images

Purpose

A common ocular manifestation, macular edema (ME) is the primary cause of visual deterioration. In this study, an artificial intelligence method based on multi-feature fusion was introduced to enable automatic ME classification on spectral-domain optical coherence tomography (SD-OCT) images, to provide a convenient method of clinical diagnosis.

Methods

First, 1,213 two-dimensional (2D) cross-sectional OCT images of ME were collected from the Jiangxi Provincial People's Hospital between 2016 and 2021. According to OCT reports of senior ophthalmologists, there were 300 images with diabetic (DME), 303 images with age-related macular degeneration (AMD), 304 images with retinal-vein occlusion (RVO), and 306 images with central serous chorioretinopathy (CSC). Then, traditional omics features of the images were extracted based on the first-order statistics, shape, size, and texture. After extraction by the alexnet, inception_v3, resnet34, and vgg13 models and selected by dimensionality reduction using principal components analysis (PCA), the deep-learning features were fused. Next, the gradient-weighted class-activation map (Grad-CAM) was used to visualize the-deep-learning process. Finally, the fusion features set, which was fused from the traditional omics features and the deep-fusion features, was used to establish the final classification models. The performance of the final models was evaluated by accuracy, confusion matrix, and the receiver operating characteristic (ROC) curve.

Results

Compared with other classification models, the performance of the support vector machine (SVM) model was best, with an accuracy of 93.8%. The area under curves AUC of micro- and macro-averages were 99%, and the AUC of the AMD, DME, RVO, and CSC groups were 100, 99, 98, and 100%, respectively.

Conclusion

The artificial intelligence model in this study could be used to classify DME, AME, RVO, and CSC accurately from SD-OCT images.

Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies [...].

Wheat lodging extraction using Improved_Unet network

The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging.

Identification lodging degree of wheat using point cloud data and convolutional neural network

Wheat is one of the important food crops, and it is often subjected to different stresses during its growth. Lodging is a common disaster in filling and maturity for wheat, which not only affects the quality of wheat grains, but also causes severe yield reduction. Assessing the degree of wheat lodging is of great significance for yield estimation, wheat harvesting and agricultural insurance claims. In particular, point cloud data extracted from unmanned aerial vehicle (UAV) images have provided technical support for accurately assessing the degree of wheat lodging. However, it is difficult to process point cloud data due to the cluttered distribution, which limits the wide application of point cloud data. Therefore, a classification method of wheat lodging degree based on dimensionality reduction images from point cloud data was proposed. Firstly, 2D images were obtained from the 3D point cloud data of the UAV images of wheat field, which were generated by dimensionality reduction based on Hotelling transform and point cloud interpolation method. Then three convolutional neural network (CNN) models were used to realize the classification of different lodging degrees of wheat, including AlexNet, VGG16, and MobileNetV2. Finally, the self-built wheat lodging dataset was used to evaluate the classification model, aiming to improve the universality and scalability of the lodging discrimination method. The results showed that based on MobileNetV2, the dimensionality reduction image from point cloud obtained by the method proposed in this paper has achieved good results in identifying the lodging degree of wheat. The F1-Score of the classification model was 96.7% for filling, and 94.6% for maturity. In conclusion, the point cloud dimensionality reduction method proposed in this study could meet the accurate identification of wheat lodging degree at the field scale.

Monitoring Wheat Lodging at Various Growth Stages

Lodging is one of the primary factors that reduce wheat yield; therefore, rapid and accurate monitoring of wheat lodging helps to provide data support for crop loss and damage response and the subsequent settlement of agricultural insurance claims. In this study, we aimed to address two problems: (1) calculating the wheat lodging area. Through comparative experiments, the SegFormer-B1 model can achieve a better segmentation effect of wheat lodging plots with a higher prediction rate and a stronger generalization ability. This model has an accuracy of 96.56%, which realizes the accurate extraction of wheat lodging plots and the relatively precise calculation of the wheat lodging area. (2) Analyzing wheat lodging areas from various growth stages. The model established, based on the mixed-stage dataset, generally outperforms those set up based on the single-stage datasets in terms of the segmentation effect. The SegFormer-B1 model established based on the mixed-stage dataset, with its mIoU reaching 89.64%, was applicable to wheat lodging monitoring throughout the whole growth cycle of wheat.

An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.