Banca de DEFESA: JOAO ALMIRO CORREA SOARES

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : JOAO ALMIRO CORREA SOARES
DATE: 31/08/2020
TIME: 14:00
LOCAL: Auditório da Pós-graduação / Videoconferência
TITLE:

DISEASE DETECTION AND PRODUCTIVITY ESTIMATE IN OIL PALM PLANTATIONS IN THE BRAZILIAN AMAZON

KEY WORDS:

Amazonia, oil palm, spectral analysis, prediction, productivity, vegetation index, precision agriculture, agricultural monitoring.

PAGES: 85
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUBÁREA: Fitossanidade
SPECIALTY: Defesa Fitossanitária
SUMMARY:

The potential of remote sensing in agricultural management in the Brazilian Amazon is still little explored despite being highly promising with the evolution of sensors and the arrival of drone platforms. Especially in the detection and spatial modeling of diseases, which in recent decades has had a strong impact on the productivity of regional cultures. The detection and monitoring of disease dispersion in oil palm plantations represent major challenges for the management of this crop in Brazil. Anticipating impacts and negative trends in oil palm production, supported by remote sensing and applicable methodologies, are among the objectives of this study at the doctoral level. Oil palm plantations in the state of Pará provide inputs for the food, cosmetics, agri-energy and biofuels industries, satisfactorily supplying the Brazilian market. In recent years, several factors such as pests, diseases and severe droughts have interfered in the productivity of oil palm in the region, generating the need to adopt new techniques for detecting and monitoring these problems. In the present study, successful spectral enhancement tests (by simple reflectance and vegetation indices) were carried out to detect diseases in four oil palm plots on the Companhia Palmares da Amazônia (CPA) farm, belonging to the Agropalma SA group, in municipality of Acará, state of Pará. The results allowed the identification of expressive minimum and maximum reflectance patterns of the studied plots, correlating them with the occurrences of diseases registered in the area. Vegetation indexes were calculated from Sentinel 2A orbital images, with emphasis on the EVI index, which showed an excellent correlation with real occurrences of diseases. However, the NDVI and SAVI indices also showed good adjustments with the occurrence of diseases in the year 2017. The areas corresponding to plots L36 and H27 showed higher occurrences of diseases, based on the reflectance analysis by vegetation indices. Thus, it could be concluded that the reflectance enhancements, NDVI, SAVI and EVI obtained by orbital sensors, are efficient in detecting diseases in the plots. The results allowed the identification of diagnostic anomalies of stresses in the plots, either due to a disease or other factor, which allows decision making in a timely manner, avoiding large scale eradication in the extensive areas of commercial oil palm plantations in the region. The second analysis of the work referred to the calculation of palm oil productivity by means of orbital images, evaluating its correlation with the actual productivity and with the geostatistical zoning of the infestation by Fatal Yellowing (FA) and anomalous climatic factors. It is worth mentioning that the remote sensing productivity estimate can be used as an important support for crop forecasting, supporting the traditional methods adopted when harvesting in the field, which in turn can be imprecise, time consuming and with high cost of execution. In this context, spectral enhancement through the calculation of vegetation indices has great potential to estimate the productivity of palm oil in the Amazon. For the calculation of productivity, orbital images of the OLI Landsat-8 system for the years 2014 and 2015 were used, covering fifteen plots in the production areas of Fazenda Companhia Palmares da Amazônia (Agropalma SA) in the municipality of Acará, state of Pará. Additionally, the Agroplama SA databases, referring to local productivity and occurrences of pests and diseases, compiled between 2005 and 2015 were used. From these databases, geostatistical analyzes (dispersion of PA), productivity calculations by remote sensing and its correlation with the infestation areas. Field data referring to productivity were collected by company employees and served to correlate with results based on remote data. Based on the images of the years 2014 and 2015, the vegetation indices were derived: NDVI, EVI, SAVI, ARVI and RNDVI, and from the statistical model of linear regression, the indices and their effectiveness for calculating productivity against the measures were evaluated. obtained in the field for both years. Such index estimates were considered satisfactory for oil palm productivity, confirming the potential efficacy of orbital remote sensing for productive crop prediction.

BANKING MEMBERS:
Presidente - 1356971 - PAULO ROBERTO SILVA FARIAS
Externa ao Programa - 2223320 - MERILENE DO SOCORRO SILVA COSTA
Externo à Instituição - ALESSANDRO FERRAZ PALMEIRA - UFRJ
Externo à Instituição - JAVIER DIAS PITA - IFPA
Externa à Instituição - ROSANA SUMIYA GURGEL - INPE