Field variability and vulnerability index to identify regional precision agriculture opportunity

2017 ◽  
Vol 19 (4) ◽  
pp. 589-605 ◽  
Author(s):  
Christopher W. Bobryk ◽  
Matt A. Yost ◽  
Newell R. Kitchen
Keyword(s):  
Precision Agriculture ◽  
Vulnerability Index ◽  
Field Variability

Introduction to Precision Agriculture

2021 ◽  
pp. 1-22
Author(s):  
Akalpita Tendulkar
Keyword(s):  
Precision Agriculture ◽  
Fertilizer Application ◽  
Farm Management ◽  
Global Positioning ◽  
Soil Variation ◽  
On Farm ◽  
Field Variability ◽  
Global Population ◽  
The Internet Of Things ◽  
Yield Monitoring

The global population is increasing at a tremendous speed; thus, the demand for safe and secure food to meet this population is in demand. Therefore, traditional farming methods are insufficient to meet this demand; thus, the next revolution in agriculture is required, which is Precision Agriculture (PA), the Fourth Agriculture Revolution. PA is a technology where the concept of farm management is based on observation, measuring, and responding to inter- and intra-field variability in crops. The technologies used for performing precision agriculture are mapping, global positioning system (GPS), yield monitoring and mapping, grid soil sampling application, variable-rate fertilizer application, remote sensing, geographic information systems (GIS), quantifying on farm variability, soil variation, variability of soil water content, time and space scales, robots, drones, satellite imagery, the internet of things, smartphone, and machine learning. Hence, the current chapter will be emphasizing the overview, concepts, history, world interest, benefits, disadvantages, and precision farming needs.

scholarly journals The accuracy of farm machinery for precision agriculture: a case for fertilizer application

1997 ◽  
Vol 45 (1) ◽  
pp. 199-215 ◽  
Author(s):  
D. Goense
Keyword(s):  
Precision Agriculture ◽  
Fertilizer Application ◽  
Positioning Accuracy ◽  
Site Specific ◽  
Positioning Systems ◽  
Application Rates ◽  
Small Influence ◽  
Variance Calculation ◽  
Field Variability ◽  
Different Levels

Work quality, capacity and reliability are important criteria for design and evaluation of farm equipment. With the introduction of precision agriculture, the ability to adapt to spatially variable soil and crop conditions, becomes an additional aspect. A calculation method was developed to find the precision of site specific fertilizer application. The variance between the required rate, RR, and the applied rate, AR, was used as a measure for precision. The theory of geo-statistics was used for variance calculation. Spreading patterns were evaluated for different levels of field variability, positioning accuracy and resolution of the required application rates. The shape of spreading patterns had small influence. The effect of the accuracy of positioning systems was dependent on the resolution of the required application rates and of the working width of independently controlled sections of the spreaders.

Conservative Precision Agriculture: an assessment of technical feasibility and energy efficiency within the LIFE+ AGRICARE project

2017 ◽  
Vol 8 (2) ◽  
pp. 439-443 ◽  
Author(s):  
Donato Cillis ◽  
Andrea Pezzuolo ◽  
Francesco Marinello ◽  
Bruno Basso ◽  
Nicola Colonna ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Precision Agriculture ◽  
Environmental Sustainability ◽  
Conservation Tillage ◽  
Conservation Agriculture ◽  
Conventional Tillage ◽  
Precision Farming ◽  
Technical Feasibility ◽  
Temporal Field ◽  
Field Variability

The integration of conservation agriculture with the benefits of precision farming represents an innovative feature aimed to achieve better economic and environmental sustainability. The synergy between these principles was assessed through a technical feasibility and energy efficiency to define the best approach depending on different agricultural systems, spatial and temporal field variability. The study compares three conservation tillage techniques supported by precision farming with conventional tillage in a specific crop rotation: wheat, rapeseed, corn and soybean. The preliminary results show a positive response of precision farming in all the conservation tillage systems, increasing yields until 22%. The energy efficiency achieves highest level in those techniques supported by precision farming, gaining peak of 9% compared to conventional tillage.

scholarly journals EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND

2018 ◽  
Vol XLII-3 ◽  
pp. 1215-1219 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
J. Jasper ◽  
G. Bareth
Keyword(s):  
Precision Agriculture ◽  
Vegetation Indices ◽  
Fertilizer Application ◽  
Forage Yield ◽  
Accurate Information ◽  
Fertilizer Treatment ◽  
Management Decisions ◽  
Fresh Matter ◽  
Negative Impacts ◽  
Field Variability

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R<sup>2</sup> values of 0.62.

WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time

2021 ◽  
pp. 243-254
Author(s):  
Balakrishna K.
Keyword(s):  
Wireless Sensor Networks ◽  
Precision Agriculture ◽  
Wireless Sensor ◽  
The Internet ◽  
Communication Model ◽  
Climatic Parameters ◽  
Statistical Software ◽  
Irrigation Optimization ◽  
Field Variability ◽  
The Internet Of Things

The use of wireless sensor networks, the internet of things, and advanced technologies lead to new direction of research in the agriculture domain called prescriptive agriculture. Prescriptive agriculture is the enforcement of precision agriculture, which is observing, measuring, and responding to inter and intra field variability of farm field. In this chapter, the advent of wireless sensor network, APSim, and communication model spurred a new direction in the farming domain at optimizing irrigation. Sensors are programmed to collect the datasets of climatic parameters such as relative humidity and temperature, where the datasets were forwarded to the server through a GSM module. Datasets collected were analyzed through statistical software for grown crops by considering inter and intra farm field conditions. Finally, information on irrigation is decimated through an algorithm designed by way2SMS and WebHost server.

scholarly journals Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

2020 ◽  
Vol 12 (3) ◽  
pp. 342
Author(s):  
Ayman Nassar ◽  
Alfonso Torres-Rua ◽  
William Kustas ◽  
Hector Nieto ◽  
Mac McKee ◽  
...  
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Precision Agriculture ◽  
Agricultural Research ◽  
Spatial Domain ◽  
Grid Size ◽  
Row Spacing ◽  
Field Scale ◽  
The Impact ◽  
Field Variability

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAirTM sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity ( u * ) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

scholarly journals Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

2019 ◽  
Vol 11 (23) ◽  
pp. 2873 ◽  
Author(s):  
Ahmed Kayad ◽  
Marco Sozzi ◽  
Simone Gatto ◽  
Francesco Marinello ◽  
Francesco Pirotti
Keyword(s):  
Machine Learning ◽  
Grain Yield ◽  
Precision Agriculture ◽  
Machine Learning Techniques ◽  
Corn Yield ◽  
Learning Techniques ◽  
Field Variability ◽  
Sentinel 2 ◽  
Corn Grain ◽  
Corn Grain Yield

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

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