Title:	Assessment of uav-based deep learning for corn crop analysis in midwest Brazil.
Authors:	MARTINS, J. A. C. HIGUTI, A. Y. H. PELLEGRIN, A. O. JULIANO, R. S. ARAUJO, A. M. de PELLEGRIN, L. A. LIESENBERG, V. RAMOS, A. P. M. GONÇALVES, W. N. SANT’ANA, D. A. PISTORI, H. MARCATO JUNIOR, J.
Affiliation:	JOSÉ AUGUSTO CORREA MARTINS, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL ALBERTO YOSHIRIKI HISANO HIGUTI, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL AIESCA OLIVEIRA PELLEGRIN, CPAP RAQUEL SOARES JULIANO, CPAP ADRIANA MELLO DE ARAUJO, CPAP LUIZ ALBERTO PELLEGRIN, CPAP VERALDO LIESENBERG, UNIVERSIDADE DO ESTADO DE SANTA CATARINA ANA PAULA MARQUES RAMOS, UNIVERSIDADE DO OESTE PAULISTA WESLEY NUNES GONÇALVES, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL DIEGO ANDRÉ SANT’ANA, INSTITUTO FEDERAL DE MATO GROSSO DO SUL HEMERSON PISTORI, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL.
Date Issued:	2024
Citation:	Agriculture, v. 14, n. 11, p. 1-15, 2024.
Description:	Abstract: Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and public managers to make informed decisions regarding crop health, yield estimation, and resource allocation in Midwest Brazil. The crops (corn) in this region are being damaged by wild pigs and other diseases. For the quantification of corn fields, this paper applies novel computer-vision techniques and a new dataset of corn imagery composed of 1416 256 × 256 images and corresponding labels. We flew nine drone missions and classified wild pig damage in ten orthomosaics in different stages of growth using semi-automatic digitizing and deep-learning techniques. The period of crop-development analysis will range from early sprouting to the start of the drying phase. The objective of segmentation is to transform or simplify the representation of an image, making it more meaningful and easier to interpret. For the objective class, corn achieved an IoU of 77.92%, and for background 83.25%, using DeepLabV3+ architecture, 78.81% for corn, and 83.73% for background using SegFormer architecture. For the objective class, the accuracy metrics were achieved at 86.88% and for background 91.41% using DeepLabV3+, 88.14% for the objective, and 91.15% for background using SegFormer.
Thesagro:	Agricultura de Precisão Milho Javali Comportamento Animal Dano Fotografia
NAL Thesaurus:	Wild boars Animal behavior Crop damage Precision agriculture
Keywords:	Drone
DOI:	https://doi.org/10.3390/agriculture14112029
Notes:	Online first.
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPAP)