scholarly journals Towards an Open Software Platform for Field Robots in Precision Agriculture

Robotics ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 207-234 ◽  
Author(s):  
Kjeld Jensen ◽  
Morten Larsen ◽  
Søren Nielsen ◽  
Leon Larsen ◽  
Kent Olsen ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Software Platform ◽  
Field Robots

Plant Classification and Position Estimation for Autonomous Field Robots

2020 ◽  
Author(s):  
Sebastian Haug
Keyword(s):  
Real World ◽  
Precision Agriculture ◽  
Image Data ◽  
Position Estimation ◽  
Single Plant ◽  
Field Robots ◽  
Weed Plants ◽  
Generic Problem ◽  
Vegetable Farming ◽  
Leaf Segmentation

This work presents new approaches to plant classifcation and plant position estimation to enable feld robot based precision agriculture. The developed methods are designed for challenging real world feld situations with small crop plants, presence of close-to-crop weed and overlap of plants. The plant classifcation system is able to distinguish two or more plant classes in feld images without the need for error-prone plant or leaf segmentation. The plant position estimation pipeline solves the generic problem of determining the position of both crop and weed plants only from image data. The combination of both methods allows feld robots to autonomously determine the type and position of plants in the feld to realize precision agriculture tasks such as single plant weed control. Experiments with a feld robot prove the applicability of the presented methods for challenging feld scenarios encountered for example in organic vegetable farming. Contents Symbols and Abbreviations  . . . . . ...

Plant Classification for Field Robots

2015 ◽  
pp. 248-272 ◽  
Author(s):  
Sebastian Haug ◽  
Jörn Ostermann
Keyword(s):  
Machine Vision ◽  
Weed Control ◽  
Precision Agriculture ◽  
System Development ◽  
Control Strategies ◽  
Classification Performance ◽  
Agricultural Robots ◽  
Field Robots ◽  
Field Environment ◽  
Average Accuracy

Small size agricultural robots which are capable of sensing and manipulating the field environment are a promising approach towards more ecological, sustainable and human-friendly agriculture. This chapter proposes a machine vision approach for plant classification in the field and discusses its possible application in the context of robot based precision agriculture. The challenges of machine vision in the field are discussed at the example of plant classification for weed control. Automatic crop/weed discrimination enables new weed control strategies where single weed plants are treated individually. System development and evaluation are done using a dataset of images captured in a commercial organic carrot farm with the autonomous field robot Bonirob under field conditions. Results indicate plant classification performance with 93% average accuracy.

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.

Precision Agriculture in the Part of Uttarakhand using Geospatial Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 2574-2579
Author(s):  
Divya Uniyal ◽  
◽  
Sourabh Dangwal ◽  
Govind Singh Negi ◽  
Saurabh Purohit ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Geospatial Techniques

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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