Subjects: Statistics >> Social Statistics submitted time 2023-12-04 Cooperative journals: 《智慧农业（中英文）》
ZHANG Gan
YAN Haifeng
HU Gensheng
ZHANG Dongyan
CHENG Tao
PAN Zhenggao
XU Haifeng
SHEN Shuhao
ZHU Keyu
Abstract： [Objective] Lodging constitutes a severe crop-related catastrophe, resulting in a reduction in photosynthesis intensity, diminished nutrient absorption efficiency, diminished crop yield, and compromised crop quality. The utilization of unmanned aerial vehicles (UAV) to acquire agricultural remote sensing imagery, despite providing high-resolution details and clear indications of crop lodging, encoun‐ters limitations related to the size of the study area and the duration of the specific growth stages of the plants. This limitation hinders the acquisition of an adequate quantity of low-altitude remote sensing images of wheat fields, thereby detrimentally affecting the performance of the monitoring model. The aim of this study is to explore a method for precise segmentation of lodging areas in limited crop growth periods and research areas. [Methods] Compared to the images captured at lower flight altitudes, the images taken by UAVs at higher altitudes cover a larger area. Consequently, for the same area, the number of images taken by UAVs at higher altitudes is fewer than those taken at lower altitudes. However, the training of deep learning models requires huge amount supply of images. To make up the issue of insufficient quantity of high-altitude UAV-acquired images for the training of the lodging area monitoring model, a transfer learning strategy was proposed. In order to verify the effectiveness of the transfer learning strategy, based on the Swin-Transformer framework, the control model, hybrid training model and transfer learning training model were obtained by training UAV images in 4 years (2019, 2020, 2021, 2023)and 3 study areas(Shucheng, Guohe, Baihe) under 2 flight altitudes (40 and 80 m). To test the model's performance, a comparative experimental approach was adopted to assess the accuracy of the three models for segmenting 80 m altitude images. The assessment relied on five metrics: intersection of union (IoU), accuracy, precision, recall, and F1-score. [Results and Discussions] The transfer learning model shows the highest accuracy in lodging area detection. Specifically, the mean IoU, accuracy, precision, recall, and F1-score achieved 85.37%, 94.98%, 91.30%, 92.52% and 91.84%, respectively. Notably, the accuracy of lodging area detection for images acquired at a 40 m altitude surpassed that of images captured at an 80 m altitude when employing a training dataset composed solely of images obtained at the 40 m altitude. However, when adopting mixed training and transfer learning strategies and augmenting the training dataset with images acquired at an 80 m altitude, the accuracy of lodging area detection for 80 m altitude images improved, inspite of the expense of reduced accuracy for 40 m altitude images. The performance of the mixed training model and the transfer learning model in lodging area detection for both 40 and 80 m altitude images exhibited close correspondence. In a cross-study area comparison of the mean values of model evaluation indices, lodging area detection accuracy was slightly higher for images obtained in Baihu area compared to Shucheng area, while accuracy for images acquired in Shucheng surpassed that of Guohe. These variations could be attributed to the diverse wheat varieties cultivated in Guohe area through drill seeding. The high planting density of wheat in Guohe resulted in substantial lodging areas, accounting for 64.99% during the late mature period. The prevalence of semi-lodging wheat further exacerbated the issue, potentially leading to misidentification of non-lodging areas. Consequently, this led to a reduction in the recall rate (mean recall for Guohe images was 89.77%, which was 4.88% and 3.57% lower than that for Baihu and Shucheng, respectively) and IoU (mean IoU for Guohe images was 80.38%, which was 8.80% and 3.94% lower than that for Baihu and Shucheng, respectively). Additionally, the accuracy, precision, and F1-score for Guohe were also lower compared to Baihu and Shucheng. [Conclusions] This study inspected the efficacy of a strategy aimed at reducing the challenges associated with the insufficient number of high-altitude images for semantic segmentation model training. By pre-training the semantic segmentation model with low-altitude images and subsequently employing high-altitude images for transfer learning, improvements of 1.08% to 3.19% were achieved in mean IoU, accuracy, precision, recall, and F1-score, alongside a notable mean weighted frame rate enhancement of 555.23 fps/m2. The approach proposed in this study holds promise for improving lodging monitoring accuracy and the speed of image segmentation. In practical applications, it is feasible to leverage a substantial quantity of 40 m altitude UAV images collected from diverse study areas including various wheat varieties for pre-training purposes. Subsequently, a limited set of 80 m altitude images acquired in specific study areas can be employed for transfer learning, facilitating the development of a targeted lodging detection model. Future research will explore the utilization of UAV images captured at even higher flight altitudes for further enhancing lodging area detection efficiency.
Subjects: Statistics >> Social Statistics submitted time 2023-12-04 Cooperative journals: 《智慧农业（中英文）》
Abstract： [Objective] Wine grapes are severely affected by leafroll disease, which affects their growth, and reduces the quality of the color, taste, and flavor of wine. Timely and accurate diagnosis of leafroll disease severity is crucial for preventing and controlling the disease, improving the wine grape fruit quality and wine-making potential. Unmanned aerial vehicle (UAV) remote sensing technology provides high-resolution images of wine grape vineyards, which can capture the features of grapevine canopies with different levels of leafroll disease severity. Deep learning networks extract complex and high-level features from UAV remote sensing images and per‐form fine-grained classification of leafroll disease infection severity. However, the diagnosis of leafroll disease severity is challenging due to the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. [Method] A novel method for diagnosing leafroll disease severity was developed at a canopy scale using UAV remote sensing technology and deep learning. The main challenge of this task was the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. To address this challenge, a method that combined deep learning fine-grained classification and generative adversarial networks (GANs) was proposed. In the first stage, the GANformer, a Transformer-based GAN model was used, to generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity. To further analyze the image generation effect of GANformer. The t-distributed stochastic neighbor embedding (t-SNE) to visualize the learned features of real and simulated images. In the second stage, the CA-Swin Transformer, an improved image classification model based on the Swin Transformer and channel attention mechanism was used, to classify the patch images into different classes of leafroll disease infection severity. CA-Swin Transformer could also use a self-attention mechanism to capture the long-range dependencies of image patches and enhance the feature representation of the Swin Transformer model by adding a channel attention mechanism after each Transformer layer. The channel attention (CA) mechanism consisted of two fully connected layers and an activation function, which could extract correlations between different channels and amplify the informative features. The ArcFace loss function and instance normalization layer was also used to enhance the fine-grained feature extraction and downsampling ability for grapevine canopy images. The UAV images of wine grape vineyards were collected and processed into orthomosaic images. They labeled into three categories: healthy, moderate infection, and severe infection using the in-field survey data. A sliding window method was used to extract patch images and labels from orthomosaic images for training and testing. The performance of the improved method was compared with the baseline model using different loss functions and normalization methods. The distribution of leafroll disease severity was mapped in vineyards using the trained CA-Swin Transformer model. [Results and Discussions] The experimental results showed that the GANformer could generate high-quality virtual canopy images of grapevines with an FID score of 93.20. The images generated by GANformer were visually very similar to real images and could produce images with different levels of leafroll disease severity. The T-SNE visualization showed that the features of real and simulated images were well clustered and separated in two-dimensional space, indicating that GANformer learned meaningful and diverse features, which enriched the image dataset. Compared to CNN-based deep learning models, Transformer-based deep learning models had more advantages in diagnosing leafroll disease infection. Swin Transformer achieved an optimal accuracy of 83.97% on the enhanced dataset, which was higher than other models such as GoogLeNet, MobileNetV2, NasNet Mobile, ResNet18, ResNet50, CVT, and T2TViT. It was found that replacing the cross entropy loss function with the ArcFace loss function improved the classification accuracy by 1.50%, and applying instance normalization instead of layer normalization further improved the accuracy by 0.30%. Moreover, the proposed channel attention mechanism, named CA-Swin Transformer, enhanced the feature representation of the Swin Transformer model, achieved the highest classification accuracy on the test set, reaching 86.65%, which was 6.54% higher than using the Swin Transformer on the original test dataset. By creating a distribution map of leafroll disease severity in vineyards, it was found that there was a certain correlation between leafroll disease severity and grape rows. Areas with a larger number of severe leafroll diseases caused by Cabernet Sauvignon were more prone to have missing or weak plants. [Conclusions] A novel method for diagnosing grapevine leafroll disease severity at a canopy scale using UAV remote sensing technology and deep learning was proposed. This method can generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity using GANformer, and classify them into different classes using CA-Swin Transformer. This method can also map the distribution of leafroll disease severity in vineyards using a sliding window method, and provides a new approach for crop disease monitoring based on UAV remote sensing technology.
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