Comparison of CWSI and T_(s)-T_(a)-VIs in moisture monitoring of dryland crops(sorghum and maize)based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.

Remote sensing-based global crop monitoring: experiences with China’s CropWatch system

Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems.

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

CAS Leading Crop Growth Monitoring

The Institute of Remote Sens-ing Applications (IRSA), apart of the Chinese Academyof Sciences (CAS), has been as-sessed as up to the world’s advancedlevel in large-scale crop monitoringby experts from the United Statesand Europe. At a recent conference jointlysponsored by CAS, the NationalAgricultural Statistics

Feasibility and reliability of agricultural crop height measurement using the laser sensor array

Crop height measurement is widely used to analyze and estimate the overall crop condition and the amount of biomass production.Not only is manual measurement on a large scale time-consuming but also it is not practical.Besides,advanced equipment is available but they require technical skills and are not reasonable for smallholders.This article investigates the feasibility of a simple and low-cost measurement system that can monitor crops height of paddy rice and wheat using laser technology.After designing and fabricating,this system was tested and evaluated in both laboratory and farm sections.In the laboratory,paddy rice height was measured,and in the field section,the height detection system measured wheat height.The results showed that the coefficient of determination(R3)between manual measurement and height detection system measurement for paddy rice was 0.96 and for wheat was 0.85.Besides,there was no significant difference between the two datasets at the level of 5%.Hence,this system can be a useful and accurate tool to monitor crops height in different growing steps.

Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water shortages.This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers.The proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and bean.The proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real time.This helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop failure.Our dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research centers.The experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,respectively.The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate.The system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

Implementation of drone technology for farm monitoring&pesticide spraying:A review

The world receives more than 200 thousand people in a day and it is expected that the total world population will reach 9.6 billion by the year 2050.This will result in extra food demand,which can only be met from enhanced crop yield.Therefore,modernization of the agricultural sector becomes the need of the hour.There are many constraints that are responsible for the low production of crops,which can be overcome by using drone technology in the agriculture sector.This paper presents an analysis of drone technologies and their modifications with time in the agriculture sector in the last decade.The application of drones in the area of crop monitoring,and pesticide spraying for Precision Agriculture(PA)has been covered.The work done related to drone structure,multiple sensor development,innovation in spot area spraying has been presented.Moreover,the use of Artificial Intelligent(AI)and deep learning for the remote monitoring of crops has been discussed.

GEE-Based monitoring method of key management nodes in cotton production

The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital forfield cotton macro-statistics,particularly for predicting cotton production and obtaining comprehensive data.This study examines Shihezi,Xinjiang as a case study,utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021.Three machine learning models(RF,SVM,and CART)were employed to extract annual crop classification area rasters,monitor weekly cultivation progress,and monitor abandoned cropland during the cultivation period.The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas,achieving the producer’s accuracy of the cotton category reached 98.5%,and the kappa coefficient is 0.947.Cotton cultivated in 2021 began is a week later than in 2020,yet exhibited a faster cultivate speed.The proportion of abandoned cottonfields in the study area rose in 2020 compared to 2019.The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle.

RESEARCH AND APPLICATION OF CROP PEST MONITORING AND EARLY WARNING TECHNOLOGY IN CHINA

The importance of food security,especially in combating the problem of acute hunger,has been underscored as a key component of sustainable development.Considering the major challenge of rapidly increasing demands for both food security and safety,the management and control of major pests is urged to secure supplies of major agricultural products.However,owing to global climate change,biological invasion(e.g.,fall armyworm),decreasing agricultural biodiversity,and other factors,a wide range of crop pest outbreaks are becoming more frequent and serious,making China,one of the world’s largest country in terms of agricultural production,one of the primary victims of crop yield loss and the largest pesticide consumer in the world.Nevertheless,the use of science and technology in monitoring and early warning of major crop pests provides better pest management and acts as a fundamental part of an integrated plant protection strategy to achieve the goal of sustainable development of agriculture.This review summarizes the most fundamental information on pest monitoring and early warning in China by documenting the developmental history of research and application,Chinese laws and regulations related to plant protection,and the National Monitoring and Early Warning System,with the purpose of presenting the Chinese model as an example of how to promote regional management of crop pests,especially of cross border pests such as fall armyworm and locust,by international cooperation across pest-related countries.

Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data

The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.

Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data

The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing injury.In this research,a typical freezing injury event occurred in Anhui Province was taken as a case study,the freezing damage degree of oilseed rape was assessed,and its development characteristics based on the vegetation metrics derived from MODIS and MERIS data were investigated.The oilseed rape was mapped according to the decline of greenness from bud stage to full-bloom period,with the phenological phases identified adopting time-series analyses.NDVI was more sensitive to freezing injury compared with other commonly used vegetation indices(VIs)calculated using MODIS bands,e.g.,EVI,GNDVI and SAVI.The freezing damage degree employing the difference between post-freeze growth and the baseline level in adjacent damage-free growing seasons was determined.The remote sensing-derived damage levels were supported by their correlation with the cold accumulated temperatures at the county level.The performance of several remote sensing indicators of plant biophysical and biochemical parameters was also investigated,i.e.,the photosynthetic rate,canopy water status,canopy chlorophyll content,leaf area index(LAI)and the red edge position(REP),in response to the advance of the freezing damage.It was found that the photosynthetic rate indicator—Photochemical Reflectance Index(PRI)responded strongly to freezing stress.Freezing injury caused canopy water loss,which could be detected though the magnitude was not very large.MERIS-LAI showed a slow and lagging response to low temperature and restored rapidly in the recovery phase;additionally,REP and the indicator of canopy chlorophyll content—MERIS Terrestrial Chlorophyll Index(MTCI),did not appear to be influenced by freezing injury.It was concluded that the physiological functions,canopy structure,and organic content metrics showed a descending order of vulnerabilities to freezing injury.

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy,such as leaf area,leaf distribution,and 3D model.The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds.This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive,unorganized LiDAR point clouds.In order to mine the distinct geometry of corn leaves and stalk,the Difference of Normal(DoN)method was proposed to extract corn leaf points.Firstly,the normals of corn leaf surface for all points were estimated on multiple scales.Secondly,the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution.Finally,the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points.The quantitative accuracy assessment showed that the overall accuracy was 94.10%,commission error was 5.89%,and omission error was 18.65%.The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive,unorganized terrestrial LiDAR point clouds using the proposed DoN method.