Banca de DEFESA: LUCAS SANTOS SANTANA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE: LUCAS SANTOS SANTANA
DATA: 25/11/2022
HORA: 13:30
LOCAL: google meet
TÍTULO:

REMOTELY PILOTED AIRCRAFT AND COMPUTER VISION APPLIED TO COFFEE GROWING MANAGEMENT

PALAVRAS-CHAVES:

Remote sensing. Image classification. Planting. Machine learning.

PÁGINAS: 80
GRANDE ÁREA: Ciências Agrárias
ÁREA: Engenharia Agrícola
SUBÁREA: Máquinas e Implementos Agrícolas
RESUMO:

Digital and precision agriculture technologies used in coffee farming have gained space and have become necessary in many coffee production stages. Among the emerging technologies, the Remotely Piloted Aircraft (RPA) can be highlighted because their products can be used as data providers for machine learning techniques and automated monitoring forms. This study aimed to apply cartographic and photogrammetric products from RPAs submitted to machine learning techniques and image analysis in digital and precision coffee farming. Four types of research were built: Application of RPA cartographic products for the coffee plant implantation project; Identification and counting of plants in PRA images; Investigations of plants development in renewal areas; and Fruit monitoring in concrete drying yard. (I)The first study evaluated different flight mission composition efficiency and point cloud levels for Digital Terrain Models generation applied in coffee plantations. Flights performed at 120 m Above Ground Land (AGL) and 80 × 80% overlap showed higher assertiveness and efficiency. The 90 m AGL flight showed great terrain detail, causing significant surface differences concerning the topography obtained by Global Navigation Satellite System (GNSS) receivers. Slope ranges up to 20% are considered reliable for precision coffee growing projects. Changes in flight settings and image processing are satisfactory for precision coffee projects. Image overlap reduction significantly lowed the processing time without influencing Digital Terrain Model DTM's quality. (II) The second research aimed to develop an algorithm for automatic counting coffee plants and define the plant's best age to carry the monitoring using RPA images. Plants with three months of development showed 86.5% count assertiveness. The best results were observed in plantations with six months of development, presenting an average of 96.8% of assertiveness in automatically counting plants. This analysis enables an algorithm development for automated counting of coffee plants through RGB images obtained by remotely piloted aircraft and machine learning applications. (III) The objective of the third research was to monitor the coffee plants' development planted on ash from crop residues through vegetative indices in RPA images, analysis of chemical elements presents in the ash and soil analysis. Preliminary results indicate the high presence of aluminum and potassium in the ash, causing significant differences in coffee development beginning. In addition, variations were observed in vegetative indices values in regions with ash presence, highlighting the NGI and NNIRI indices. The research developed by this paper provides essential information for digital agriculture technologies advancement in coffee growing.

MEMBROS DA BANCA:
Externo ao Programa - RUBENS JOSE GUIMARAES - DAG/ESAL (Membro)
Externo ao Programa - RAFAEL DE OLIVEIRA FARIA - DEG/EENG (Membro)
Externo à Instituição - MURILO MACHADO DE BARROS - UFLA (Suplente)
Externo à Instituição - MARLEY LAMOUNIER MACHADO - EMBRAPA (Membro)
Externo à Instituição - LUIS CARLOS CIRILO CARVALHO - UDESC (Membro)
Presidente - GABRIEL ARAUJO E SILVA FERRAZ (Membro)
Externo ao Programa - ADAO FELIPE DOS SANTOS - DAG/ESAL (Suplente)