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Detection Method of Apple Alternaria Leaf Spot Based on Deep-Semi-NMF
Zhuojun Fu, Zheng Hu, Yangjun Deng, Chenfeng Long, Xinghui Zhu
Smart Agriculture 2024, 6(6): 144-154
Published: 01 November 2024
Abstract PDF (10.3 MB) Collect
Downloads：55
Objective

Apple Alternaria leaf spot can easily lead to premature defoliation of apple tree leaves, thereby affecting the quality and yield of apples. Consequently, accurately detecting of the disease has become a critical issue in the precise prevention and control of apple tree diseases. Due to factors such as backlighting, traditional image segmentation-based methods for detecting disease spots struggle to accurately identify the boundaries of diseased areas against complex backgrounds. There is an urgent need to develop new methods for detecting apple Alternaria leaf spot, which can assist in the precise prevention and control of apple tree diseases.

Methods

A novel detection method named Deep Semi-Non-negative Matrix Factorization-based Mahalanobis Distance Anomaly Detection (DSNMFMAD) was proposed, which combines Deep Semi-Non-negative Matrix Factorization (DSNMF) with Mahalanobis distance for robust anomaly detection in complex image backgrounds. The proposed method began by utilizing DSNMF to extract low-rank background components and sparse anomaly features from the apple Alternaria leaf spot images. This enabled effective separation of the background and anomalies, mitigating interference from complex background noise while preserving the non-negativity constraints inherent in the data. Subsequently, Mahalanobis distance was employed, based on the Singular Value Decomposition (SVD) feature subspace, to construct a lesion detector. The detector identified lesions by calculating the anomaly degree of each pixel in the anomalous regions. The apple tree leaf disease dataset used was provided by PaddlePaddle AI-Studio. Each image in the dataset has a resolution of 512×512 pixels, in RGB color format, and was in JPEG format. The dataset was captured in both laboratory and natural environments. Under laboratory conditions, 190 images of apple leaves with spot-induced leaf drop were used, while 237 images were collected under natural conditions. Furthermore, the dataset was augmented with geometric transformations and random changes in brightness, contrast, and hue, resulting in 1145 images under laboratory conditions and 1419 images under natural conditions. These images reflect various real-world scenarios, capturing apple leaves at different stages of maturity, in diverse lighting conditions, angles, and noise environments. This diversed dataset ensured that the proposed method could be tested under a wide range of practical conditions, providing a comprehensive evaluation of its effectiveness in detecting apple Alternaria leaf spot.

Results and Discussions

DSNMFMAD demonstrated outstanding performance under both laboratory and natural conditions. A comparative analysis was conducted with several other detection methods, including GRX (Reed-Xiaoli detector), LRX (Local ReedXiaoli detector), CRD (Collaborative-Representation-Based Detector), LSMAD (LRaSMD-Based Mahalanobis Distance Detector), and the deep learning model Unet. The results demonstrated that DSNMFMAD exhibited superior performance in the laboratory environment. The results demonstrated that DSNMFMAD attained a recognition accuracy of 99.8% and a detection speed of 0.0872 s/image. The accuracy of DSNMFMAD was found to exceed that of GRX, LRX, CRD, LSMAD, and Unet by 0.2%, 37.9%, 10.3%, 0.4%, and 24.5%, respectively. Additionally, the DSNMFMAD exhibited a substantially superior detection speed in comparison to LRX, CRD, LSMAD, and Unet, with an improvement of 8.864, 107.185, 0.309, and 1.565 s, respectively. In a natural environment, where a dataset of 1419 images of apple Alternaria leaf spot was analysed, DSNMFMAD demonstrated an 87.8% recognition accuracy, with an average detection speed of 0.0910 s per image. In this case, its accuracy outperformed that of GRX, LRX, CRD, LSMAD, and Unet by 2.5%, 32.7%, 5%, 14.8%, and 3.5%, respectively. Furthermore, the detection speed was faster than that of LRX, CRD, LSMAD, and Unet by 2.898, 132.017, 0.224, and 1.825 s, respectively.

Conclusions

The DSNMFMAD proposed in this study was capable of effectively extracting anomalous parts of an image through DSNMF and accurately detecting the location of apple Alternaria leaf spot using a constructed lesion detector. This method achieved higher detection accuracy compared to the benchmark methods, even under complex background conditions, demonstrating excellent performance in lesion detection. This advancement could provide a valuable technical reference for the detection and prevention of apple Alternaria leaf spot.
Issue
Segmenting and grading the blast disease of rice leaves using VCDM-UNet
Yangjun DENG, Xu WANG, Chenfeng LONG, Jinling LIU, Xinghui ZHU, Siqiao TAN
Transactions of the Chinese Society of Agricultural Engineering 2024, 40(23): 190-198
Published: 15 December 2024
Abstract PDF (1 MB) Collect
Downloads：31

Rice is one of the main food crops in China. An efficient and accurate assessment of leaf blast disease severity is crucial to the early prevention, precision application of pesticides, and yield loss prediction in rice. In this study, the indica rice variety CO39 at the three-leaf stage was selected as the research sample. The samples were collected and placed in a laboratory environment for image acquisition using a Nikon D3200 camera. A high-quality dataset was constructed for the rice leaf blast segmentation and disease severity grading. Subsequently, the training set was enhanced through some operations, such as contrast enhancement, flipping and rotation, regional erasure, and deformation. An improved UNet model (vgg16 coordinate dropout focal-dice mixed loss UNet, VCDM-UNet) was proposed to segment the leaf blast. The high efficiency and strong subjectivity were achieved in disease grading, compared with the traditional. Firstly, VGG16 was utilized as the backbone network of UNet to extract features of leaves and lesions, particularly for irregular lesion shapes. Secondly, the CA (coordinate attention) mechanism and Dropout were introduced in the upsampling module, in order to enhance the generalization of the model on the leaf and lesion pixels. Thirdly, LFD (focal-dice mixed loss) was adopted to improve the loss function. Sample imbalance was optimized to mitigate the impact of a large proportion of background pixels on model learning. Small proportions of leaves and lesions were then identified. The experimental results were as follows: 1) The segmentation effectiveness of the VCDM-UNet model was validated on rice leaf lesions. UNet, PSPNet, and DeepLabV3+ were employed on a test dataset to compare their segmentation. PSPNet exhibited significant deficiencies in producing noticeably incomplete edges when segmenting leaf images and rendering them largely unusable. Although DeepLabV3+ and UNet were fully capable of segmenting the leaf edges, the insufficient lesion feature was also captured, leading to suboptimal segmentation. In contrast to the rest three models, VCDM-UNet demonstrated greater accuracy in lesion segmentation, and closely matching manual annotation, thus showing a distinct advantage. 2) The VCDM-UNet was achieved a mIoU (mean intersection over union) of 82.93%, mPA (mean pixel accuracy) of 88.87%, and F1-Macro of 89.96%, indicating the improvements of 12.43, 11.43, and 9.64 percentage points over PSPNet, 6.97, 7.32, and 5.52 percentage points over DeepLabV3+, and 3.37, 4.60, and 2.59 percentage points over UNet, respectively, with the best feature extraction for leaves and lesions. In terms of model segmentation efficiency, the number of parameters was reduced by 82.8, 113.8, and 22.8 M, compared with PSPNet, DeepLabV3, and UNet, respectively. Nevertheless, the prediction time also increased by 1.00, 0.38, and 0.21 s, respectively. Considering that segmentation accuracy was more important than segmentation efficiency in the tasks of disease image segmentation, indicating that the slight increase was reasonable in prediction time. 3) The severity of rice leaf blast disease was further assessed, according to the GB/T15790-2009 standard and the leaf blast disease severity calculation formula. In this case, the average grading accuracy of VCDM-UNet reached 83.95%, thus outperforming the comparative models. It was verified that the improved model can provide technical support to the grading of rice leaf blast disease severity. Overall, the improved model demonstrated the better performance on individual and whole rice leaves, even with a relatively small sample size. The finding can lay the foundation for the practical application of large-scale leaf blast disease grading in the next steps. Additionally, since leaf recognition in natural environments was influenced by more complex factors, such as the diversity of leaf postures, mutual occlusion between leaves, and multiple diseases on a single leaf, it is necessary to expand the sample size and diversity of rice leaf data in the future. Further improvements to the model are also required to enhance its adaptability and robustness in complex environments.

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