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.

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 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.

scholarly journals Spatial variability of soil fertility in an integrated crop livestock forest system

2017 ◽  
Vol 8 (2) ◽  
pp. 590-593 ◽  
Author(s):  
A. C. C. Bernardi ◽  
G. M. Bettiol ◽  
G. G. Mazzuco ◽  
S. N. Esteves ◽  
P. P. A. Oliveira ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Fertility ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Acid Soil ◽  
Management Strategies ◽  
Fertilizer Application ◽  
Management Tools ◽  
Site Specific ◽  
The Tropics

Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

Implementing precision agriculture concepts and technologies into crop production and site-specific management of nematodes.

2021 ◽  
pp. 421-427
Author(s):  
Michael T. Plumblee ◽  
John D. Mueller
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Spatial Location ◽  
Future Research ◽  
Economic Returns ◽  
Site Specific ◽  
Management Zones ◽  
Application Rates ◽  
Other Information ◽  
Specific Management

Abstract Precision agriculture is defined as a management strategy that gathers, processes and analyses temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production. This includes a wide range of technologies, many of which are linked to geographic information system technologies used to analyse spatial location and organize layers of on-farm data. Southern root-knot (Meloidogyne incognita), reniform (Rotylenchulus reniformis), Columbia lance (Hoplolaimus columbus) and sting (Belonolaimus longicaudatus) nematodes are significant problems on cotton in the US. Granular and fumigant nematicides have provided control when applied at uniform rates across fields pre-plant in-furrow or at-plant in-furrow at costs of US$148 and US$74 per hectare, respectively. Site-specific variable-rate (SSVR) technologies offer producers the potential to move away from uniform application rates and apply nematicides only to specific management zones in a field. The goal is to sustain yield levels while minimizing nematicide applications and thus increasing economic returns. This chapter discusses strategies for the development of management zones, evolution of application technologies needed for SSVR applications, assessment of nematode damage from multispectral images, and field experiences with site-specific nematode management. The economic importance of precision agriculture technology and future research requirements are also mentioned.

scholarly journals Approbation of elements of the informational and analytical crop management system

2021 ◽  
Vol 937 (2) ◽  
pp. 022081
Author(s):  
N V Gritz ◽  
A V Dichensky ◽  
R A Rostovtsev
Keyword(s):  
Precision Agriculture ◽  
Management System ◽  
Crop Management ◽  
Fertilizer Application ◽  
Federal State ◽  
State Budget ◽  
Fiber Flax ◽  
Research Institution ◽  
Application Rates ◽  
Working Bodies

Abstract The object of the research is the module of the Informational and Analytical Crop Management System with differentiated fertilization. The aim of the research was to study the features of the implementation of the functionality of the Information and Analytical Crop Management System (IACMS) with differentiated fertilization. The key element of using the capabilities of the Information and Analytical Crop Management System for is the digitization of fields and the creation of their electronic maps. Differentiated fertilization was carried out in accordance with electronic maps compiled during the agrochemical survey. Digital maps of the fields were entered into the on-board computer of the tractor, equipped with additional devices for the implementation of the differential fertilization mechanism. In accordance with the main goal of the research, the tasks of checking the automated calculation of fertilizer application rates were solved for fiber-flax on the field belonged to Federal State Budget Research Institution «Federal Research Center for Bast Fiber Crops», preparation of a task-map for differential fertilization in CSV-format files and containing the number of the elementary plot and the value of the applied fertilizers, compatibility of the task-map of differentiated fertilization with the equipment of LLC “Center of Precision Agriculture «Aerosoyuz» (LLC «CPA «Aerosoyuz»»), aggregation of equipment with Russian technologies, the interaction of the working bodies of equipment for applying fertilizers with (IACMS), the possibility of controlled passage of the equipment, differentiated fertilization in compliance with the norms of task-maps.

Environmental Benefits of Precision Agriculture Adoption

2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi
Keyword(s):  
Environmental Impacts ◽  
Precision Agriculture ◽  
New Technologies ◽  
Environmental Benefits ◽  
Herbicide Application ◽  
Pesticide Application ◽  
Policy Makers ◽  
Application Rates ◽  
Biodiversity Policy ◽  
Lime Application

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.

scholarly journals Evaluation of the Regional-Scale Optimal K Rate Based on Sustainable Apple Yield and High-Efficiency K Use in Loess Plateau and Bohai Bay of China: A Meta-Analysis

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1368
Author(s):  
Wenzheng Tang ◽  
Wene Wang ◽  
Dianyu Chen ◽  
Ningbo Cui ◽  
Haosheng Yang ◽  
...  
Keyword(s):  
Loess Plateau ◽  
High Efficiency ◽  
Meta Analysis ◽  
Regional Scale ◽  
Fertilizer Application ◽  
Application Rate ◽  
Bohai Bay ◽  
The Loess Plateau ◽  
Application Rates ◽  
Relationship Model

In order to meet the growing food demand of the global population and maintain sustainable soil fertility, there is an urgent need to optimize fertilizer application amount in agricultural production practices. Most of the existing studies on the optimal K rates for apple orchards were based on case studies and lack information on optimizing K-fertilizer management on a regional scale. Here, we used the method of combining meta-analysis with the K application rate-yield relationship model to quantify and summarize the optimal K rates of the Loess Plateau and Bohai Bay regions in China. We built a dataset based on 159 observations obtained from 18 peer-reviewed literature studies distributed in 15 different research sites and evaluated the regional-scale optimal K rates for apple production. The results showed that the linear plus platform model was more suitable for estimating the regional-scale optimal K rates, which were 208.33 and 176.61 kg K ha−1 for the Loess Plateau and Bohai Bay regions of China, respectively. Compared with high K application rates, the optimal K rates increased K use efficiency by 45.88–68.57%, with almost no yield losses. The optimal K rates also enhanced the yield by 6.30% compared with the low K application rates.

scholarly journals Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield

2021 ◽  
Vol 13 (14) ◽  
pp. 8059
Author(s):  
Calogero Schillaci ◽  
Tommaso Tadiello ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Precision Agriculture ◽  
Stem Elongation ◽  
Maize Yield ◽  
Application Rate ◽  
N Fertilization ◽  
Economic Benefits ◽  
Growth Monitoring ◽  
Fixed Rate ◽  
Positioning Systems ◽  
Significant Difference

Proximal sensing represents a growing avenue for precision fertilization and crop growth monitoring. In the last decade, precision agriculture technology has become affordable in many countries; Global Positioning Systems for automatic guidance instruments and proximal sensors can be used to guide the distribution of nutrients such as nitrogen (N) fertilization using real-time applications. A two-year field experiment (2017–2018) was carried out to quantify maize yield in response to variable rate (VR) N distribution, which was determined with a proximal vigour sensor, as an alternative to a fixed rate (FR) in a cereal-livestock farm located in the Po valley (northern Italy). The amount of N distributed for the FR (140 kg N ha−1) was calculated according to the crop requirement and the regional regulation: ±30% of the FR rate was applied in the VR treatment according to the Vigour S-index calculated on-the-go from the CropSpec sensor. The two treatments of N fertilization did not result in a significant difference in yield in both years. The findings suggest that the application of VR is more economically profitable than the FR application rate, especially under the hypothesis of VR application at a farm scale. The outcome of the experiment suggests that VR is a viable and profitable technique that can be easily applied at the farm level by adopting proximal sensors to detect the actual crop N requirement prior to stem elongation. Besides the economic benefits, the VR approach can be regarded as a sustainable practice that meets the current European Common Agricultural Policy.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

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