Heguang Sun

Henan Agricultural University

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i-10 index

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Co-Authors

Wei Guo

Henan Agricultural University

Xiaoyu Song

National Engineering Research Center for Information Technology in Agriculture

Haikuan Feng

National Engineering Research Center for Information Technology in Agriculture

Chunkai Zheng

National Engineering Research Center for Information Technology in Agriculture

Guijun Yang

National Engineering Research Center for Information Technology in Agriculture

Lin Zhou

Anhui Agricultural University

Di Pan

National Engineering Research Center for Information Technology in Agriculture

Zheng Gong

University of Electronic Science and Technology of China

Jie Zhang

Chinese Academy of Sciences

Pingping Li

Nanjing Forestry University

Cooperative Institutions

National Engineering Research Center for Information Technology in Agriculture

Henan Agricultural University

Ministry of Agriculture and Rural Affairs

Chinese Academy of Sciences

Beijing Academy of Agricultural and Forestry Sciences

Henan Polytechnic University

Xi'an University of Science and Technology

Beijing Normal University

Shenyang Jianzhu University

Zhejiang University of Technology

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Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Agriculture (2024)

Heguang Sun Lin Zhou Meiyan Shu Jie Zhang Ziheng Feng

Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75).

Oversampling

10.3390/agriculture14030476

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Citations (2)

Estimation of Fusarium Head Blight Severity Based on Transfer Learning

Agronomy (2022)

Chunfeng Gao Zheng Gong Xingjie Ji Mengjia Dang Qiang He

The recognition accuracy of traditional image recognition methods is heavily dependent on the design of complicated and tedious hand-crafted features. In view of the problems of poor accuracy and complicated feature extraction, this study presents a methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and convolutional neural networks (CNNs). Firstly, we utilized the potent feature learning and feature expression capabilities of CNNs to realize the automatic learning of FHB characteristics. Using transfer learning technology, VGG16, ResNet50, and MobileNetV1 models were pre-trained on the ImageNet. The knowledge was transferred to the estimation of FHB severity, and the fully connected (FC) layer of the models was modified. Secondly, acquiring the wheat images at the peak of the outbreak of FHB as the research object, after preprocessing for size filling on the wheat images, the image dataset was expanded with operations such as mirror flip, rotation transformation, and superimposed noise to improve the performance of the model and reduce the overfitting of models. Finally, under the Tensorflow deep learning framework, the VGG16, ResNet50, and MobileNetV1 models were subjected to transfer learning. The results showed that in the case of transfer learning and data augmentation, the ResNet50 model in Accuracy, Precision, Recall, and F1 score was better than the other two models, giving the highest accuracy of 98.42% and F1 score of 97.86%. The ResNet50 model had the highest recognition accuracy, providing technical support and reference for the accurate recognition of FHB.

Transfer of learning

Overfitting

Feature (linguistics)

10.3390/agronomy12081876

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Citations (14)

Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning

Agriculture (2023)

Wei Guo Heguang Sun Hongbo Qiao Hui Zhang Lin Zhou

Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF770~780, 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight.

Kappa

Continuous wavelet transform

10.3390/agriculture13081504

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Citations (3)

Inversion of biophysical parameters of potato based on an active learning pool-based sampling strategy

International Journal of Remote Sensing (2024)

Yuanyuan Ma Xiaoyu Song Jie Zhang Di Pan Haikuan Feng

10.1080/01431161.2024.2413027

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Citations (0)

Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery

Agriculture (2023)

Chunfeng Gao Xingjie Ji Qiang He Zheng Gong Heguang Sun

Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB.

Multispectral pattern recognition

10.3390/agriculture13020293

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Citations (22)

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Agronomy (2023)

Yuanyuan Ma Chunxia Qiu Jie Zhang Di Pan Chunkai Zheng

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

10.3390/agronomy13123071

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Citations (5)

Rice Grain Protein Content Monitoring Based on Texture Features of Uav Hyperspectral Images

Jie Zhang Xiaoyu Song Xia Jing Yuanyuan Ma Heguang Sun

Grain Protein Content (GPC) is an important index to evaluate rice's grain quality. Remote sensing monitoring of rice grain protein content using a UAV-mounted high-throughput sensor is an efficient means under the requirements of precision agriculture. In this study, the UAV-based hyperspectral image was obtained, the image texture and texture-derived index of the hyperspectral image at the critical growth stage of rice were calculated, the hyperspectral texture characteristics under different nitrogen treatments were explored, the texture characteristics and texture index sensitive to GPC were screened, and finally, a single parameter and multiple stepwise regression model was established. The potential of hyperspectral texture features and texture index for GPC estimation was evaluated. The results showed that the texture characteristics showed a specific change trend with the change in nitrogen level. The four original texture characteristics were highly correlated with GPC at near 670nm or after 730nm at the panicle initiation stage and heading stage, while only a few bands were correlated with GPC at near 670nm at the tillering stage. The correlation coefficient between the texture index and GPC reached above 0.8 in most linear combinations in each stage. In the single-parameter GPC estimation model, the texture index can effectively improve the estimation accuracy of GPC. When GPC estimation is performed in each period, model R2>0.8 and RMSE<0.4. When multiple sensitive feature combinations were screened and modeled by multiple stepwise regression methods, the model estimation accuracy was improved again. The optimal GPC monitoring accuracy R2=0.836(RMSE=0.363%) was performed at the tillering stage. At the stage of panicle initiation, the optimal monitoring precision was R2 = 0.891% (RMSE = 0.301%). When GPC was monitored at the heading stage, the optimal monitoring accuracy was R2=0.922(RMSE=0.251%). The findings indicate that hyperspectral texture characteristics from UAV-based hyperspectral images can offer more features sensitive to grain protein content (GPC), hence facilitating a more precise assessment of GPC in rice. This approach also presents a viable means for remotely monitoring rice quality information through remote sensing techniques.

Texture (cosmology)

Content (measure theory)

10.2139/ssrn.4621999

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Citations (0)

Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation

Computers and Electronics in Agriculture (2023)

Heguang Sun Xiaoyu Song Wei Guo Mei Guo Yanzhi Mao

Drone

RGB color model

Cohen's kappa

10.1016/j.compag.2023.108438