scholarly journals Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Mohamad Awad
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Optimization Model ◽  
Crop Yields ◽  
Potato Crop ◽  
Optimization Techniques ◽  
Yield Estimation ◽  
Climate Conditions ◽  
New Model

Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

scholarly journals Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3787 ◽  
Author(s):  
Bing Yu ◽  
Songhao Shang
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Irrigation District ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Distribution Maps

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

scholarly journals Effects of nitrogen and phosphorus fertilizer on crop yields in a field pea-spring wheat-potato rotation system with calcareous soil in semi-arid environments

2016 ◽  
Vol 14 (2) ◽  
pp. e1101 ◽  
Author(s):  
Chang-An Liu ◽  
Sen Zhang ◽  
Shuai Hua ◽  
Xin Rao
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Calcareous Soil ◽  
Crop Yields ◽  
Potato Crop ◽  
Field Pea ◽  
Arid Environments ◽  
Annual Average ◽  
N Treatment ◽  
Semi Arid

The object of the present study was to investigate the yield-affecting mechanisms influenced by N and P applications in rainfed areas with calcareous soil. The experimental treatments were as follows: NF (no fertilizer), N (nitrogen), P (phosphorus), and NP (nitrogen plus phosphorus) in a field pea-spring wheat-potato cropping system. This study was conducted over six years (2003-2008) on China’s semi-arid Loess Plateau. The fertilizer treatments were found to decrease the soil water content more than the NF treatment in each of the growing seasons. The annual average yields of the field pea crops during the entire experimental period were 635, 677, 858, and 1117 kg/ha for the NF, N, P, and NP treatments, respectively. The annual average yields were 673, 547, 966, and 1056 kg/ha for the spring wheat crops for the NF, N, P, and NP treatments, respectively. Also, the annual average yields were 1476, 2120, 1480, and 2424 kg/ha for the potato crops for the NF, N, P, and NP treatments, respectively. In the second cycle of the three-year rotation, the pea and spring wheat yields in the P treatment were 1.2 and 2.8 times higher than that in the N treatment, respectively. Meanwhile, the potato crop yield in the N treatment was 3.1 times higher than that in the P treatment. In conclusion, the P fertilizer was found to increase the yields of the field pea and wheat crops, and the N fertilizer increased the potato crop yield in rainfed areas with calcareous soil.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

Cotton Yield Estimation from UAV-Based Plant Height

2019 ◽  
Vol 62 (2) ◽  
pp. 393-404 ◽  
Author(s):  
Aijing Feng ◽  
Meina Zhang ◽  
Kenneth A. Sudduth ◽  
Earl D. Vories ◽  
Jianfeng Zhou
Keyword(s):  
Remote Sensing ◽  
Plant Height ◽  
Crop Yield ◽  
Low Cost ◽  
Reference Points ◽  
Cotton Field ◽  
Accurate Estimation ◽  
Cotton Yield ◽  
Yield Estimation ◽  
Sensing System

Abstract. Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, existing in-field methods for estimating crop yield are not efficient. The goal of this research was to evaluate the performance of a UAV-based remote sensing system with a low-cost RGB camera to estimate cotton yield based on plant height. The UAV system acquired images at 50 m above ground level over a cotton field at the first flower growth stage. Waypoints and flight speed were selected to allow >70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field that was used to extract plant height by calculating the difference in elevation between the crop canopy and bare soil surface. Twelve ground reference points with known height were deployed in the field to validate the UAV-based height measurement. Geo-referenced yield data were aligned to the plant height map based on GPS and image features. Correlation analysis between yield and plant height was conducted row-by-row with and without row registration. Pearson correlation coefficients between yield and plant height with row registration for all individual rows were in the range of 0.66 to 0.96 and were higher than those without row registration (0.54 to 0.95). A linear regression model using plant height was able to estimate yield with root mean square error of 550 kg ha-1 and mean absolute error of 420 kg ha-1. Locations with low yield were analyzed to identify the potential reasons, and it was found that water stress and coarse soil texture, as indicated by low soil apparent electricity conductivity (ECa), might contribute to the low yield. The findings indicate that the UAV-based remote sensing system equipped with a low-cost digital camera was potentially able to monitor plant growth status and estimate cotton yield with acceptable errors. Keywords: Cotton, Geo-registration, Plant height, UAV-based remote sensing, Yield estimation.

scholarly journals Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

2021 ◽  
Vol 13 (6) ◽  
pp. 1094
Author(s):  
Xingshuo Peng ◽  
Wenting Han ◽  
Jianyi Ao ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Estimation Accuracy ◽  
Corn Yield ◽  
Sensing Data ◽  
Water Treatments

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

Remote Sensing based comparative analysis of cotton irrigation and water productivity in Pakistan, Turkey and Uzbekistan

2020 ◽  
Author(s):  
Muhammad Usman ◽  
Talha Mahmood ◽  
Christopher Conrad
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Crop Yields ◽  
Water Productivity ◽  
The Other ◽  
Cotton Yield ◽  
Crop Water ◽  
Yield Estimation ◽  
Consumptive Water Use ◽  
Consumptive Water

<p>Textile products made with cotton produced in Pakistan, Turkey, and Uzbekistan are largely imported to European markets. This is responsible for high virtual water imports from these countries and thus puts immense pressure on their water resources, which is further extravagated due to climate change and population growth. The solution to combat the issue, on one hand, is to cut water usage for cotton irrigation, and on the other hand, to increase water productivity. The biggest challenge in this regard is the correct quantification of consumptive water use, cotton yield estimation and crop water productivities at a finer spatial resolution on regional levels, which is now possible by utilizing remote sensing (RS) data and approaches. It can also facilitate comparing regions of interest, like in this study, Pakistan, Turkey, and Uzbekistan by utilizing similar data and techniques. For the current study, MODIS data along with various climatic variables were utilized for the estimation of consumptive water use and cotton yield estimation by employing SEBAL and Light Use Efficiency (LUE) models, respectively. These estimations were then used for working out water productivities of different regions of selected countries as case studies. The results show that the study area in Turkey achieved maximum cotton water productivity (i.e. 0.75 - 1.2 kg.m<sup>-3</sup>) followed by those in Uzbekistan (0.05 – 0.85 kg.m<sup>-3</sup>) and Pakistan (0.04 – 0.23 kg.m<sup>-3</sup>).  The variability is higher for Uzbekistan possibly due to agricultural transition post-soviet-union era. In the case of Pakistan, the lower cotton water productivities are mainly attributed to lower crop yields (400 – 1200 kg.ha<sup>-1</sup>) in comparison to Turkey (3850 – 5800 kg.ha<sup>-1</sup>) and Uzbekistan (450 – 2500 kg.ha<sup>-1</sup>). Although the highest crop water productivity is achieved for the study region in Turkey, there is still potential for further improvement by introducing on-farm water management. In the case of the other two countries, especially for Pakistan, major improvements are possible through maximizing crop yields. The next steps include comparisons of the results in economic out-turns.</p>

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