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Guijun Yang
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

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Journal article
Published: 05 August 2021 in Remote Sensing
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Apple (Malus domestica Borkh. cv.Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (VIs)-based random forest (RFVI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RFNDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RFNDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RFNDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

ACS Style

Xueyuan Bai; Zhenhai Li; Wei Li; Yu Zhao; Meixuan Li; Hongyan Chen; Shaochong Wei; Yuanmao Jiang; Guijun Yang; Xicun Zhu. Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries. Remote Sensing 2021, 13, 3073 .

AMA Style

Xueyuan Bai, Zhenhai Li, Wei Li, Yu Zhao, Meixuan Li, Hongyan Chen, Shaochong Wei, Yuanmao Jiang, Guijun Yang, Xicun Zhu. Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries. Remote Sensing. 2021; 13 (16):3073.

Chicago/Turabian Style

Xueyuan Bai; Zhenhai Li; Wei Li; Yu Zhao; Meixuan Li; Hongyan Chen; Shaochong Wei; Yuanmao Jiang; Guijun Yang; Xicun Zhu. 2021. "Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries." Remote Sensing 13, no. 16: 3073.

Journal article
Published: 27 July 2021 in Agricultural and Forest Meteorology
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Accurate and timely crop yield forecasts can provide essential information to make conclusive agricultural policies and to conduct investments. Recent studies have used different machine learning techniques to develop such yield forecast systems for single crops at regional scales. However, no study has used multiple sources of environmental predictors (climate, soil, and vegetation) to forecast yields for three major crops in China. In this study, we adopted 7-year observed crop yield data (2013–2019) for three major grain crops (wheat, maize, and rice) across China, and three major data sets including climate, vegetation indices, and soil properties were used to develop a dynamic yield forecasting system based on the random forest (RF) model. The RF model showed good performance for estimating yields of all three crops with correlation coefficient (r) higher than 0.75 and normalized root means square errors (nRMSE) lower than 18.0%. Our results also showed that crop yields can be satisfactorily forecasted at one to three months prior to harvest. The optimum lead time for yield forecasting depended on crop types. In addition, we found the major predictors influencing crop yield varied between crops. In general, solar radiation and vegetation indices (especially during jointing to milk development stages) were identified as the main predictor for winter wheat; vegetation indices (throughout the growing season) and drought (especially during emergence to tasseling stages) were the most important predictors for spring maize; soil moisture (throughout the growing season) was the dominant predictor for summer maize, late rice, and mid rice; precipitation (especially during booting to heading stages) was the main predictor for early rice. Our study provides insights into practical crop yield forecasting and the understanding of yield response to environmental conditions at a large scale across China. The methods undertaken in this research can be easily implemented in other countries with available information on climate, soil, and vegetation conditions.

ACS Style

Linchao Li; Bin Wang; Puyu Feng; Huanhuan Wang; Qinsi He; Yakai Wang; De Li Liu; Yi Li; Jianqiang He; Hao Feng; Guijun Yang; Qiang Yu. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology 2021, 308-309, 108558 .

AMA Style

Linchao Li, Bin Wang, Puyu Feng, Huanhuan Wang, Qinsi He, Yakai Wang, De Li Liu, Yi Li, Jianqiang He, Hao Feng, Guijun Yang, Qiang Yu. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology. 2021; 308-309 ():108558.

Chicago/Turabian Style

Linchao Li; Bin Wang; Puyu Feng; Huanhuan Wang; Qinsi He; Yakai Wang; De Li Liu; Yi Li; Jianqiang He; Hao Feng; Guijun Yang; Qiang Yu. 2021. "Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China." Agricultural and Forest Meteorology 308-309, no. : 108558.

Project report
Published: 23 July 2021 in Remote Sensing
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Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

ACS Style

Stefano Pignatti; Raffaele Casa; Giovanni Laneve; Zhenhai Li; Linyi Liu; Pablo Marzialetti; Nada Mzid; Simone Pascucci; Paolo Silvestro; Massimo Tolomio; Deepak Upreti; Hao Yang; Guijun Yang; Wenjiang Huang. Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources. Remote Sensing 2021, 13, 2889 .

AMA Style

Stefano Pignatti, Raffaele Casa, Giovanni Laneve, Zhenhai Li, Linyi Liu, Pablo Marzialetti, Nada Mzid, Simone Pascucci, Paolo Silvestro, Massimo Tolomio, Deepak Upreti, Hao Yang, Guijun Yang, Wenjiang Huang. Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources. Remote Sensing. 2021; 13 (15):2889.

Chicago/Turabian Style

Stefano Pignatti; Raffaele Casa; Giovanni Laneve; Zhenhai Li; Linyi Liu; Pablo Marzialetti; Nada Mzid; Simone Pascucci; Paolo Silvestro; Massimo Tolomio; Deepak Upreti; Hao Yang; Guijun Yang; Wenjiang Huang. 2021. "Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources." Remote Sensing 13, no. 15: 2889.

Journal article
Published: 08 May 2021 in Remote Sensing
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Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data.

ACS Style

Fa Zhao; Guijun Yang; Xiaodong Yang; Haiyan Cen; Yaohui Zhu; Shaoyu Han; Hao Yang; Yong He; ChunJiang Zhao. Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data. Remote Sensing 2021, 13, 1836 .

AMA Style

Fa Zhao, Guijun Yang, Xiaodong Yang, Haiyan Cen, Yaohui Zhu, Shaoyu Han, Hao Yang, Yong He, ChunJiang Zhao. Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data. Remote Sensing. 2021; 13 (9):1836.

Chicago/Turabian Style

Fa Zhao; Guijun Yang; Xiaodong Yang; Haiyan Cen; Yaohui Zhu; Shaoyu Han; Hao Yang; Yong He; ChunJiang Zhao. 2021. "Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data." Remote Sensing 13, no. 9: 1836.

Journal article
Published: 21 April 2021 in Remote Sensing
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With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

ACS Style

Yaohui Zhu; Guijun Yang; Hao Yang; Fa Zhao; Shaoyu Han; Riqiang Chen; Chengjian Zhang; Xiaodong Yang; Miao Liu; Jinpeng Cheng; ChunJiang Zhao. Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China. Remote Sensing 2021, 13, 1630 .

AMA Style

Yaohui Zhu, Guijun Yang, Hao Yang, Fa Zhao, Shaoyu Han, Riqiang Chen, Chengjian Zhang, Xiaodong Yang, Miao Liu, Jinpeng Cheng, ChunJiang Zhao. Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China. Remote Sensing. 2021; 13 (9):1630.

Chicago/Turabian Style

Yaohui Zhu; Guijun Yang; Hao Yang; Fa Zhao; Shaoyu Han; Riqiang Chen; Chengjian Zhang; Xiaodong Yang; Miao Liu; Jinpeng Cheng; ChunJiang Zhao. 2021. "Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China." Remote Sensing 13, no. 9: 1630.

Journal article
Published: 06 February 2021 in Remote Sensing
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Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

ACS Style

Yuanyuan Fu; Guijun Yang; Xiaoyu Song; Zhenhong Li; Xingang Xu; Haikuan Feng; ChunJiang Zhao. Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sensing 2021, 13, 581 .

AMA Style

Yuanyuan Fu, Guijun Yang, Xiaoyu Song, Zhenhong Li, Xingang Xu, Haikuan Feng, ChunJiang Zhao. Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sensing. 2021; 13 (4):581.

Chicago/Turabian Style

Yuanyuan Fu; Guijun Yang; Xiaoyu Song; Zhenhong Li; Xingang Xu; Haikuan Feng; ChunJiang Zhao. 2021. "Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis." Remote Sensing 13, no. 4: 581.

Review article
Published: 04 February 2021 in European Journal of Agronomy
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Nitrogen (N) is significantly related to crop photosynthetic capacity. Over-and-under-application of N fertilizers not only limits crop productivity but also leads to negative environment impacts. With such a dilemma, a feasible solution is to match N supply with crop needs across time and space. Hyperspectral remote sensing has been gradually regarded as a cost-effective alternative to traditional destructive field sampling and laboratory testing for crop N status determination. Hyperspectral vegetation indices (VIs) and linear nonparametric regression have been the dominant techniques used to estimate crop N status. Machine learning algorithms have gradually exerted advantages in modelling the non-linear relationships between spectral data and crop N. Physically-based methods were rarely used due to the lack of radiative transfer models directly involving N. The existing crop N retrieval methods rely heavily on the relationship between chlorophyll and N. The underlying mechanisms of using protein as a proxy of N and crop protein retrieval from canopy hyperspectral data need further exploration. A comprehensive survey of the existing N-related hyperspectral VIs was made with the aim to provide guidance in VI selection for practical application. The combined use of feature mining and machine learning algorithms was emphasized in the overview. Some feature mining methods applied in the field of classification and chemometrics might be adapted for extracting crop N-related features. The deep learning algorithms need further exploration in crop N status assessment from canopy hyperspectral data. Finally, the major challenges and further development direction in crop N status assessment were discussed. The overview could provide a theoretical and technical support to promote applications of hyperspectral remote sensing in crop N status assessment.

ACS Style

Yuanyuan Fu; Guijun Yang; Ruiliang Pu; Zhenhai Li; Heli Li; Xingang Xu; Xiaoyu Song; Xiaodong Yang; ChunJiang Zhao. An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives. European Journal of Agronomy 2021, 124, 126241 .

AMA Style

Yuanyuan Fu, Guijun Yang, Ruiliang Pu, Zhenhai Li, Heli Li, Xingang Xu, Xiaoyu Song, Xiaodong Yang, ChunJiang Zhao. An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives. European Journal of Agronomy. 2021; 124 ():126241.

Chicago/Turabian Style

Yuanyuan Fu; Guijun Yang; Ruiliang Pu; Zhenhai Li; Heli Li; Xingang Xu; Xiaoyu Song; Xiaodong Yang; ChunJiang Zhao. 2021. "An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives." European Journal of Agronomy 124, no. : 126241.

Journal article
Published: 16 December 2020 in Computers and Electronics in Agriculture
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Accurately identifying black point disease in wheat kernels from random samples within digital images is a fundamental and challenging task in disease identification. The performance of traditional methods is satisfactory in homogeneous environments, but their performance decreases when they are applied to images acquired in dynamic ones. In this paper, a multifeature-based machine learning method is proposed to identify and evaluate the incidence of black point disease. Ten wheat cultivars with different resistances to disease were selected to verify the accuracy of the method. First, a marker-based watershed algorithm was used to separate wheat kernels from the background to accomplish the coarse segmentation. After patches were generated from the coarse segmentation results, the patches were labeled manually and divided into two categories: black point areas and healthy areas. Gabor and Canny operators were used for texture and shape features respectively to build a feature matrix. Then, a classification model based on a naive Bayes classifier was trained to recognize the differences between the two types of patches by their features. The proposed model finally achieved the correct classification of each pixel from the testing sample and output the results in the form of a binary image, thus accomplishing the segmentation of the image. Finally, the severity of the disease was calculated according to the proportion of minimum circumscribed areas of the disease and the total area of the wheat kernel. Through the above operations, the incidence of black point disease in random samples can be determined. Five indicators, Qseg, Sr, Precision, Recall, and F-measure, were used to evaluate the segmentation effects. The average accuracy of segmentation results for the testing samples were 0.85, 0.89, 0.87, 0.86, and 83% respectively. Compared with other segmentation approaches, including the excess green method, the excess green minus excess red method, the color index of vegetation extraction, and two traditional threshold segmentation methods known as Otsu and maximum entropy threshold, the proposed algorithm had greater segmentation accuracy. Moreover, this method was demonstrated to be robust enough to be used for different illumination conditions, shooting angles, and image resolutions.

ACS Style

Chengquan Zhou; Guijun Yang; Dong Liang; Jun Hu; Hao Yang; Jibo Yue; Ruirui Yan; Liang Han; Linsheng Huang; Lijun Xu. Recognizing black point in wheat kernels and determining its extent using multidimensional feature extraction and a naive Bayes classifier. Computers and Electronics in Agriculture 2020, 180, 105919 .

AMA Style

Chengquan Zhou, Guijun Yang, Dong Liang, Jun Hu, Hao Yang, Jibo Yue, Ruirui Yan, Liang Han, Linsheng Huang, Lijun Xu. Recognizing black point in wheat kernels and determining its extent using multidimensional feature extraction and a naive Bayes classifier. Computers and Electronics in Agriculture. 2020; 180 ():105919.

Chicago/Turabian Style

Chengquan Zhou; Guijun Yang; Dong Liang; Jun Hu; Hao Yang; Jibo Yue; Ruirui Yan; Liang Han; Linsheng Huang; Lijun Xu. 2020. "Recognizing black point in wheat kernels and determining its extent using multidimensional feature extraction and a naive Bayes classifier." Computers and Electronics in Agriculture 180, no. : 105919.

Journal article
Published: 30 November 2020 in Foods
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Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient(R2v) of 0.93 and root mean square error(RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

ACS Style

Fan Wang; ChunJiang Zhao; Guijun Yang. Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods. Foods 2020, 9, 1778 .

AMA Style

Fan Wang, ChunJiang Zhao, Guijun Yang. Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods. Foods. 2020; 9 (12):1778.

Chicago/Turabian Style

Fan Wang; ChunJiang Zhao; Guijun Yang. 2020. "Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods." Foods 9, no. 12: 1778.

Journal article
Published: 18 November 2020 in Remote Sensing
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Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

ACS Style

Yuanyuan Fu; Guijun Yang; Zhenhai Li; Xiaoyu Song; Zhenhong Li; Xingang Xu; Pei Wang; ChunJiang Zhao. Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression. Remote Sensing 2020, 12, 3778 .

AMA Style

Yuanyuan Fu, Guijun Yang, Zhenhai Li, Xiaoyu Song, Zhenhong Li, Xingang Xu, Pei Wang, ChunJiang Zhao. Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression. Remote Sensing. 2020; 12 (22):3778.

Chicago/Turabian Style

Yuanyuan Fu; Guijun Yang; Zhenhai Li; Xiaoyu Song; Zhenhong Li; Xingang Xu; Pei Wang; ChunJiang Zhao. 2020. "Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression." Remote Sensing 12, no. 22: 3778.

Journal article
Published: 02 November 2020 in Remote Sensing
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The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

ACS Style

Chengjian Zhang; Youyi Jiang; Bo Xu; Xiao Li; Yaohui Zhu; Lei Lei; Riqiang Chen; Zhen Dong; Hao Yang; Guijun Yang. Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR. Remote Sensing 2020, 12, 3592 .

AMA Style

Chengjian Zhang, Youyi Jiang, Bo Xu, Xiao Li, Yaohui Zhu, Lei Lei, Riqiang Chen, Zhen Dong, Hao Yang, Guijun Yang. Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR. Remote Sensing. 2020; 12 (21):3592.

Chicago/Turabian Style

Chengjian Zhang; Youyi Jiang; Bo Xu; Xiao Li; Yaohui Zhu; Lei Lei; Riqiang Chen; Zhen Dong; Hao Yang; Guijun Yang. 2020. "Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR." Remote Sensing 12, no. 21: 3592.

Journal article
Published: 05 October 2020 in Sustainability
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The impact of agricultural cooperatives on apple farmers’ technical efficiency (TE) in China was examined. The cooperatives were divided into two groups: a collective marketing group for farmers and an equivalent non-marketing group that did not provide a marketing service, although other functions remained the same. Using the propensity score matching (PSM) procedure and stochastic production frontier (SPF) modelling, cooperatives’ key functions that potentially increase farmers’ TE can be identified. The results indicate that membership of either group is positively related to yield. However, cooperatives that were not engaged in marketing achieved higher TE than non-members. This suggests that policy makers should encourage cooperatives to focus on activities that do not include direct marketing to increase TE in apple production in China.

ACS Style

Ruopin Qu; Yongchang Wu; Jing Chen; Glyn Jones; Wenjing Li; Shan Jin; Qian Chang; Yiying Cao; Guijun Yang; Zhenhong Li; Lynn Frewer. Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis. Sustainability 2020, 12, 8194 .

AMA Style

Ruopin Qu, Yongchang Wu, Jing Chen, Glyn Jones, Wenjing Li, Shan Jin, Qian Chang, Yiying Cao, Guijun Yang, Zhenhong Li, Lynn Frewer. Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis. Sustainability. 2020; 12 (19):8194.

Chicago/Turabian Style

Ruopin Qu; Yongchang Wu; Jing Chen; Glyn Jones; Wenjing Li; Shan Jin; Qian Chang; Yiying Cao; Guijun Yang; Zhenhong Li; Lynn Frewer. 2020. "Effects of Agricultural Cooperative Society on Farmers’ Technical Efficiency: Evidence from Stochastic Frontier Analysis." Sustainability 12, no. 19: 8194.

Review
Published: 29 September 2020 in Remote Sensing
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The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

ACS Style

Ning Zhang; Guijun Yang; Yuchun Pan; Xiaodong Yang; Liping Chen; ChunJiang Zhao. A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sensing 2020, 12, 3188 .

AMA Style

Ning Zhang, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen, ChunJiang Zhao. A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sensing. 2020; 12 (19):3188.

Chicago/Turabian Style

Ning Zhang; Guijun Yang; Yuchun Pan; Xiaodong Yang; Liping Chen; ChunJiang Zhao. 2020. "A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades." Remote Sensing 12, no. 19: 3188.

Journal article
Published: 14 August 2020 in Sensors
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Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

ACS Style

Linsheng Huang; Yuanyuan Zhang; Guijun Yang; Dong Liang; Heli Li; Zhenhai Li; Xiaodong Yang. Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model. Sensors 2020, 20, 4570 .

AMA Style

Linsheng Huang, Yuanyuan Zhang, Guijun Yang, Dong Liang, Heli Li, Zhenhai Li, Xiaodong Yang. Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model. Sensors. 2020; 20 (16):4570.

Chicago/Turabian Style

Linsheng Huang; Yuanyuan Zhang; Guijun Yang; Dong Liang; Heli Li; Zhenhai Li; Xiaodong Yang. 2020. "Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model." Sensors 20, no. 16: 4570.

Short communication
Published: 08 August 2020 in Artificial Intelligence in Agriculture
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Big data provide a pathway to lower crop nitrogen inputs from genetic breeding to field production. Moreover, multidisciplinary efforts from plant health sensing, deep machine learning and cloud computing can integrate multi-source data to form information and knowledge. So Big data analysis as a prospective optimal method, will make leaps towards addressing future issues of sustainable agriculture.

ACS Style

Guijun Yang; Yanbo Huang; ChunJiang Zhao. Agri-BIGDATA: A smart pathway for crop nitrogen inputs. Artificial Intelligence in Agriculture 2020, 4, 150 -152.

AMA Style

Guijun Yang, Yanbo Huang, ChunJiang Zhao. Agri-BIGDATA: A smart pathway for crop nitrogen inputs. Artificial Intelligence in Agriculture. 2020; 4 ():150-152.

Chicago/Turabian Style

Guijun Yang; Yanbo Huang; ChunJiang Zhao. 2020. "Agri-BIGDATA: A smart pathway for crop nitrogen inputs." Artificial Intelligence in Agriculture 4, no. : 150-152.

Methods article
Published: 03 June 2020 in Frontiers in Plant Science
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The automated harvesting of strawberry brings benefits such as reduced labor costs, sustainability, increased productivity, less waste, and improved use of natural resources. The accurate detection of strawberries in a greenhouse can be used to assist in the effective recognition and location of strawberries for the process of strawberry collection. Furthermore, being able to detect and characterize strawberries based on field images is an essential component in the breeding pipeline for the selection of high-yield varieties. The existing manual examination method is error-prone and time-consuming, which makes mechanized harvesting difficult. In this work, we propose a robust architecture, named “improved Faster-RCNN,” to detect strawberries in ground-level RGB images captured by a self-developed “Large Scene Camera System.” The purpose of this research is to develop a fully automatic detection and plumpness grading system for living plants in field conditions which does not require any prior information about targets. The experimental results show that the proposed method obtained an average fruit extraction accuracy of more than 86%, which is higher than that obtained using three other methods. This demonstrates that image processing combined with the introduced novel deep learning architecture is highly feasible for counting the number of, and identifying the quality of, strawberries from ground-level images. Additionally, this work shows that deep learning techniques can serve as invaluable tools in larger field investigation frameworks, specifically for applications involving plant phenotyping.

ACS Style

Chengquan Zhou; Jun Hu; Zhifu Xu; Jibo Yue; Hongbao Ye; Guijun Yang. A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique. Frontiers in Plant Science 2020, 11, 1 .

AMA Style

Chengquan Zhou, Jun Hu, Zhifu Xu, Jibo Yue, Hongbao Ye, Guijun Yang. A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique. Frontiers in Plant Science. 2020; 11 ():1.

Chicago/Turabian Style

Chengquan Zhou; Jun Hu; Zhifu Xu; Jibo Yue; Hongbao Ye; Guijun Yang. 2020. "A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique." Frontiers in Plant Science 11, no. : 1.

Journal article
Published: 25 May 2020 in Computers and Electronics in Agriculture
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Manual measurement and visual inspection is a common practice for acquiring crop data in orchards and is a labor-intensive, time-consuming, and costly task. Accurate and rapid acquisition of crop data is vital for monitoring the dynamics of tree growth and optimizing farm management. In this work, we present a technique for orchard data acquisition and analysis that uses remote imagery acquired from unmanned aerial vehicles (UAVs) combined with deep learning convolutional neural networks to automatically detect and segment individual trees and measure the crown width, perimeter, and crown projection area of apple trees. By using an UAV platform, 50 high-resolution images of apple trees were collected from an orchard during dormancy (bare branches), and then each apple tree was detected by using a Faster R-CNN object detector. Based on these results, each tree was segmented by using a U-Net deep learning network. After convex tree boundaries were extracted from the semantic segmentation results by using an efficient pruning strategy, the crown parameters were automatically calculated, and the accuracy was compared with that obtained by manual delineation. The results show that the proposed remote sensing technique can be used to detect and count apple trees with precision and recall of 91.1% and 94.1%, respectively, segment their branches with an overall accuracy of 97.1%, and estimate crown parameter with an overall accuracy exceeding 92%. We conclude that this method not only saves labor by avoiding field measurements but also allows growers to dynamically monitor the growth of orchard trees.

ACS Style

Jintao Wu; Guijun Yang; Hao Yang; Yaohui Zhu; Zhenhai Li; Lei Lei; ChunJiang Zhao. Extracting apple tree crown information from remote imagery using deep learning. Computers and Electronics in Agriculture 2020, 174, 105504 .

AMA Style

Jintao Wu, Guijun Yang, Hao Yang, Yaohui Zhu, Zhenhai Li, Lei Lei, ChunJiang Zhao. Extracting apple tree crown information from remote imagery using deep learning. Computers and Electronics in Agriculture. 2020; 174 ():105504.

Chicago/Turabian Style

Jintao Wu; Guijun Yang; Hao Yang; Yaohui Zhu; Zhenhai Li; Lei Lei; ChunJiang Zhao. 2020. "Extracting apple tree crown information from remote imagery using deep learning." Computers and Electronics in Agriculture 174, no. : 105504.

Journal article
Published: 20 May 2020 in Sensors
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Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400–1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

ACS Style

Linsheng Huang; Taikun Li; Chuanlong Ding; Jinling Zhao; Dongyan Zhang; Guijun Yang. Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion. Sensors 2020, 20, 2887 .

AMA Style

Linsheng Huang, Taikun Li, Chuanlong Ding, Jinling Zhao, Dongyan Zhang, Guijun Yang. Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion. Sensors. 2020; 20 (10):2887.

Chicago/Turabian Style

Linsheng Huang; Taikun Li; Chuanlong Ding; Jinling Zhao; Dongyan Zhang; Guijun Yang. 2020. "Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion." Sensors 20, no. 10: 2887.

Journal article
Published: 01 May 2020 in Agriculture
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Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

ACS Style

Longfei Zhou; Xiaohe Gu; Shu Cheng; Guijun Yang; Meiyan Shu; Qian Sun. Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data. Agriculture 2020, 10, 146 .

AMA Style

Longfei Zhou, Xiaohe Gu, Shu Cheng, Guijun Yang, Meiyan Shu, Qian Sun. Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data. Agriculture. 2020; 10 (5):146.

Chicago/Turabian Style

Longfei Zhou; Xiaohe Gu; Shu Cheng; Guijun Yang; Meiyan Shu; Qian Sun. 2020. "Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data." Agriculture 10, no. 5: 146.

Methods article
Published: 15 April 2020 in Frontiers in Plant Science
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Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head. By constructing a private image dataset with 100s of broccoli-head images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named “Improved ResNet” was trained to extract the broccoli pixels from the background. Then, a yield estimation model was built based on the number of extracted pixels and the corresponding pixel weight value. Additionally, the Particle Swarm Optimization Algorithm (PSOA) and the Otsu method were applied to grade the quality of each broccoli head according to our new standard. The trained model achieved an Accuracy of 0.896 on the test set for broccoli head segmentation, demonstrating the feasibility of this approach. When testing the model on a set of images with different light intensities or with some noise, the model still achieved satisfactory results. Overall, our approach of training a deep learning model using low-cost imaging devices represents a means to improve broccoli breeding and vegetable trade.

ACS Style

Chengquan Zhou; Jun Hu; Zhifu Xu; Jibo Yue; Hongbao Ye; Guijun Yang. A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks. Frontiers in Plant Science 2020, 11, 1 .

AMA Style

Chengquan Zhou, Jun Hu, Zhifu Xu, Jibo Yue, Hongbao Ye, Guijun Yang. A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks. Frontiers in Plant Science. 2020; 11 ():1.

Chicago/Turabian Style

Chengquan Zhou; Jun Hu; Zhifu Xu; Jibo Yue; Hongbao Ye; Guijun Yang. 2020. "A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks." Frontiers in Plant Science 11, no. : 1.