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Agriculture Applications

Code: EGEO/AGR4000     Acronym: EGEO/AGR4000

Keywords
Classification Keyword
OFICIAL Agrarian Sciences
OFICIAL Surveying Engineering

Instance: 2021/2022 - 2S

Active? Yes
Web Page: https://www.fc.up.pt/pessoas/mccunha/
Responsible unit: Department of Geosciences, Environment and Spatial Plannings
Course/CS Responsible: Master's degree in Remote Sensing

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
M:DR 4 The study plan from 2018 1 - 3 21 81

Teaching Staff - Responsibilities

Teacher Responsibility
Mário Manuel de Miranda Furtado Campos Cunha

Teaching - Hours

Theoretical and practical : 1,00
Other: 0,50
Type Teacher Classes Hour
Theoretical and practical Totals 1 1,00
Renan Tosin 0,75
Mário Manuel de Miranda Furtado Campos Cunha 0,25
Other Totals 1 0,50
Mário Manuel de Miranda Furtado Campos Cunha 0,50

Teaching language

Suitable for English-speaking students

Objectives

This UC presents the main concepts, techniques and applications of remote sensing in precision agriculture in a perspective of both inference of agronomic processes and production of spatio-temporal information (mapping and monitoring).

After successful completion of this course students are expected to be able to:

  1. Learn the basic principles and techniques used in Remote Sensing applications to precision agriculture
  2. Become aware of the enormous potential of Remote Sensing techniques for the development of efficient agronomic processes (economic and environmental) and monitoring of agriculture in a perspective of precision agriculture;
  3. Demonstrate knowledge on specialized remote sensing topics taught in the course
  4. Analyse the use of innovative earth observation techniques for various agricultural applications
  5. Create state-of-the art remote sensing solutions for specific agricultural applications
  6. Show awareness of innovative remote sensing developments for agriculture
  7. Critically evaluate remote sensing research applied during the course
  8. Apply the acquired knowledge through cases studies
  9. Set-up research under supervision in a project and reflect on the results obtained
  10. Communicate the outcome of the project through a technical-scientific report.

Learning outcomes and competences

The program was designed to meet the learning goals of this course. The correspondence between each objective and the topic of the program which aims to accomplish it is provided below.

Points 1) e 2) of the program aim to reach objectives 1) and 2).

Points 3) and 4) of the program aim to reach objectives 3), 4) and 5).

Overall, all topics of the program contribute to achieve objectives 6) and 7):

Point 5) of the program aims to reach objective 8), 9) and 10).

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

n.a.

Program

- Remote sensing (RS) of agriculture processes and environments: terminology, concepts, overview of process and technologies, value in decision making and the main applications. Precision agriculture.

- Main remote sensing platforms and sensors used in agricultural applications.

- Typology of available remote sensing data and suitability for different agricultural applications.

- Processing of remote sensing data used in agriculture, such as spectral vegetation indices, modelling crop biophysical processes/attributes using diverse approaches (statistical models, energy balance models and radiative transfer models), temporal dynamics of crops (phenology) and agricultural geophysics.

- Analysis of the main agricultural applications in a perspective of precision agriculture: inference of agronomic processes (e.g. diseases detection), mapping and monitoring of variables of agricultural interest (e.g. irrigation management).

- Assessment and handling of RS data for several agricultural applications based on case studies are central in the practical.

Mandatory literature

*; *

Comments from the literature

There is no single textbook for this course. Students will be referred to: Texts and journal scientific articles on electronic reserve at UP library and posted on the class Moodle site, other websites, and to software tutorials and documentation. Students will have access to the tutorials and documentation supporting the use of software applications specifically developed for this course.

Teaching methods and learning activities

Theoretical classes are mainly based on Power Point presentations. Some of the topics are taught in the form of Seminars presented by specialists in the field.

In the Practical classes, a set of case studies are proposed, aiming to apply and consolidate the knowledge acquired in the theoretical classes. The practical includes field work, assessment, processing and data analysis of RS data, as well as reporting and requires synthesizing knowledge from different disciplines. In the practical classes students acquire skills for the use of specific computer tools in agricultural applications of remote sensing.

Teaching methods will be more tutorial (cases studies) than lecturing

Software

MATLAB (Toolbox: 'Image Processing', 'Statistics and Machine Learning' and the 'Parallel Computing)
R Studio
Quantum GIS (QGIS)
Sentinel Application Platform (SNAP)

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 20,00
Trabalho de campo 15,00
Trabalho prático ou de projeto 65,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Elaboração de projeto 43,00
Estudo autónomo 21,00
Trabalho de campo 2,00
Trabalho escrito 15,00
Total: 81,00

Eligibility for exams

Presence in at least 75% of the classes provided: .verify FCUP norms https://sigarra.up.pt/up/LEGISLACAO_GERAL.ver_legislacao?p_nr=4025

- The students with frequent and approval in the practical work in previous years will be admitted to the final exam, without having to perform the practical work again.

 - All students, even students with special frequency (e.g. students work, mobility) must deliver and present the practical assignments provided for the UC before taking the exam in any of the periods (“normal”, “recurso” e “especial”)

Calculation formula of final grade

Evaluation formula: CF = WE *0.20 + CS*0.80.

- Final grade must be equal to or greater than 10, and
- Minimum classification of 8 values in any of the tests;

Case studies (CS):

- Acquiring and pre-processing remote sensing data from different sensors and platforms.

- Processing of spectral vegetation indices based on several formulations.

- Modelling and mapping crop biophysical attributes based on earth observation satellite (EOS) data. Students will develop a case study based on real data (e.g. irrigation schedule, productivity, nutritional status…)

 

Examinations or Special Assignments

To access the examination of the special season the student will have to carry out all the practical works planned. The student who wishes to do this test (special season exam) should contact the Professor before the exam (1 week) so that the protocols are available to carry out the practical work.

Special assessment (TE, DA, ...)

Working students have to do all practical assignments before they take the exam (“Época normal”) or exam “recurso”.

In order to access to the special examination, the working students must complete all the work foreseen in the UC. The student who intends to do this test must contact the Professor before this exam (1 week) so that the protocols are available for the practical work.

Classification improvement

In order to improve the final grade, the student must complete all the components (WE and CS). It is not possible to make classifications improvements in partial tests.

The student who wishes to do this test (improvement of final grade) should contact the Professor before the exam (1 week) so that the protocols are available to carry out the practical work.

 

Observations

Working students and other students with special regime should clarify the teacher of their situation right at the beginning of the CU.
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