Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

Remote sensing technology is the important tool of digital earth,it can facilitate nutrient management in sustainable cropping systems.In the study,two types of radial basis function(RBF)neural network approaches,the standard radial basis function(SRBF)neural networks and the modified type of RBF,generalized regression neural networks(GRNN),were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices(VIs)and hyperspectral reflectance.Comparison analyses were performed to the spectral variables and the approaches.The Root Mean Square Error(RMSE)and determination coefficients(R2)were used to assess their predictability of nitrogen concentrations.For all spectral variables(VIs and hyperspectral reflectance),the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations,and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN method.This indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen concentrations.Among the VIs,the Modified Chlorophyll Absorption in Reflectance Index(MCARI),MCARI1510,and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen concentrations.Compared to the results from VIs,the hyperspectral reflectance data also gave an acceptable estimation.The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method,especially the GRNN method,is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.

Waterlogging risk assessment for winter wheat using multi-source data in the middle and lower reaches of Yangtze River

Waterlogging is a serious agro-meteorological disaster caused by excessive soil water,which usually causes tremendous crop yield losses.The region of middle and lower reaches of Yangtze River in China is an important production base of winter wheat,and is an area prone to waterlogging.The risk assessment of winter wheat waterlogging can provide more thorough understanding about the risk-prone environment related with food safety in this region.This study combined a variety of environmental and agricultural factors and assessed the waterlogging risk of winter wheat from the aspects of sensitivity of hazard formative environments,hazard risk,and vulnerability of hazard-affected body using multi-source data.Furthermore,it constructed a compound waterlogging risk assessment model to classify the study area into high,relatively high,moderate,and low risky areas,respectively.The results showed that the proposed model could more comprehensively reflect the occurrence mechanism of winter wheat waterlogging by synchronizing geographical,agricultural,and meteorological factors.The waterlogging regionalization based on the model could reasonably represent the spatial distribution and differentiate regional characteristics of winter wheat waterlogging in the study area.

Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images

Crop-type identification is one of the most significant applications of agricultural remote sensing,and it is important for yield estimation prediction and field management.At present,crop identification using datasets from unmanned aerial vehicle(UAV)and satellite platforms have achieved state-of-the-art performances.However,accurate monitoring of small plants,such as the coffee flower,cannot be achieved using datasets from these platforms.With the development of time-lapse image acquisition technology based on ground-based remote sensing,a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated,which can provide great opportunities for small target monitoring of a specific region.The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network(CNN)model to improve coffee flower identification accuracy using the time-lapse images(i.e.,digital images).A certain number of positive and negative samples are selected from the original digital images for the network model training.Then,the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets.Based on the well-trained CNN model,the coffee flower is initially extracted,and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm.Based on the digital images with different depression angles and illumination conditions,the performance of the proposed method is investigated by comparison of the performances of support vector machine(SVM)and CNN model.Hence,the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy.The results of the image with a 52.5°angle of depression under soft lighting conditions are the highest,and the corresponding Dice(F1)and intersection over union(IoU)have reached 0.80 and 0.67,respectively.