Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images

https://doi.org/10.1016/j.compag.2017.10.006Get rights and content

Highlights

  • We developed a new method for mapping skips in sugarcane fields using UAV images.

  • Object-based image analysis performed and semi-automatic procedure developed.

  • High-resolution UAV images provide accurate information on sugarcane skip incidence.

Abstract

The use of unmanned aerial vehicles (UAVs) as remote sensing platforms has tremendous potential for describing detailed site-specific features of crops, especially in early post-emergence, which was not possible previously with satellite images. This article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The procedure consists of three consecutive phases: (1) identification of sugarcane planting rows, (2) identification of the existent sugarcane within the crop rows, and (3) skip extraction and creation of field-extent crop maps. Results based on experimental fields achieved skip rates of between 2.29% and 10.66%, indicating a planting operation with excellent and good quality, respectively. The relationship of estimated versus observed skip length had a coefficient of determination of 0.97, which was confirmed by the value of the enhanced Wilmott concordance coefficient of 0.92, indicating good agreement. The OBIA procedure allowed a high level of automation and adaptability, and it provided useful information for decision making, agricultural monitoring, and the reduction of operational costs.

Introduction

In agriculture, the availability of reliable and timely information on crop condition and the possibility of problems, assists proper planning decisions, which in turn contribute to increased profitability and reduced costs (Zhang et al., 2002).

Currently, because of the current demand for biofuels and sugar production, Brazil has large areas of agricultural land covered by sugarcane fields (Martinelli and Filoso, 2008, Nassar et al., 2008, Martinelli et al., 2011). Thus, developments of techniques and technologies aimed at operational efficiency, cost reduction, and yield increase have attracted considerable attention because of its economic and environmental importance (Abdel-Rahman and Ahmed, 2008, Bégué et al., 2010, Bocca et al., 2015).

The sugarcane cycle is semi-perennial with a growth cycle of approximately 12 or 18 months. After the first harvest, the ratoons are harvested annually for a period of about 5–7 years (Xavier et al., 2006, Rudorff et al., 2010). Therefore, to maintain the longevity and productivity of the crop field during this period, monitoring of the quality of the planting and harvesting operations is essential (Lebourgeois et al., 2010, Bocca et al., 2015). Matsuoka and Stolf (2012, p.148) defined “Gappy fields”, in terms of either plant canes or ratoons, as “a signal of bad crop management in most cases: bad soil preparation, inappropriate cultivar, improper seed cane, improper season for planting, unexpected disease or pest occurrence, herbicide damage, salinity, bad mechanical operations, mainly during harvest in the case of ratoons, if set aside climatic factors like freezing temperatures, lightening and extreme drought.”

The identification and quantification of skips in sugarcane fields is of great importance because it assesses the uniformity of germination and tillering and the consequent formation of stalks, which in turn is correlated directly with yield (Matsuoka and Stolf, 2012, Bocca et al., 2015). In addition to measuring the quality of the planting operation, quantification of skips provides support for decision making regarding the replanting or renovation of the field, in order to guarantee its continued profitability (Keerthipala and Dharmawardene, 2000, Matsuoka and Stolf, 2012). Currently, the presence of skips in planting rows is verified by in situ visual inspection. However, these inspections are generally conducted from the field borders and thus, heterogeneity across the entire crop field might affect the accuracy of such estimates (Bocca et al., 2015).

Recently, the use of remote sensing technologies for agricultural monitoring has been gaining ground, because they can provide spatially and temporally distributed information objectively and quickly over a variety of scales (Zhang et al., 2002, Ahamed et al., 2011, Mulla, 2013). The development of new technologies such as unmanned aerial vehicles (UAVs) as platforms for the acquisition of remote sensing imagery, allows some of the limitations of orbital and airborne platforms that hinder crop monitoring in real-time to be overcome, e.g., the suitability of revisit times, avoidance of cloud cover, costs, complexity of operation, and limitation of spatial resolution (Berni et al., 2009, Everaerts, 2009, Zhang and Kovacs, 2012, Colomina and Molina, 2014). These characteristics make UAV platforms suitable for a number of applications, including crop monitoring (Hunt et al., 2005, Hunt et al., 2010, Torres-Sánchez et al., 2014, Torres-Sánchez et al., 2015a, Comba et al., 2015), weed detection (Torres-Sánchez et al., 2013, Peña et al., 2015), water stress assessment (Berni et al., 2009, Zarco-Tejada et al., 2012), disease detection (Garcia-Ruiz et al., 2013), and yield estimation (Swain et al., 2010), i.e., applications where time-critical management is required.

UAVs can provide imagery with very high spatial resolution of only a few centimeters and they allow images to be acquired at optimal moments for the desired purposes, which makes them ideal for distinguishing crop plants during their first stages of development (Hengl, 2006, López-Granados, 2011, Torres-Sánchez et al., 2015a). However, very-high-resolution images require powerful image analysis procedures because, unlike lower resolution images, single pixels might no longer capture the characteristics of the classification targets. Additionally, these images show higher intra-class spectral variability and subsequently, a reduction in the degree of statistical separability among the classes compared with conventional pixel-based classification methods (Yu et al., 2006, Castillejo-González et al., 2014, Peña et al., 2015, Torres-Sánchez et al., 2015a). To overcome this limitation and to attain a high level of automation and adaptability, object-based image analysis (OBIA) has been used successfully with high-resolution satellite imagery (Novack et al., 2010, de Castro et al., 2013, Castillejo-González et al., 2014) and UAV imagery (Laliberte and Rango, 2011, Peña et al., 2013, Peña et al., 2015, Diaz-Varela et al., 2014, Qin, 2014, Torres-Sánchez et al., 2015a, Torres-Sánchez et al., 2015b). The OBIA approach first identifies spatially and spectrally homogeneous units called “objects”, which are created by grouping adjacent pixels following a segmentation process, and then using the created “objects” as the basic elements for analysis. Thus, it is possible to create automated and auto-adaptive classification methods by combining the spectral, contextual, morphological, and hierarchical information of these elements (Blaschke, 2010).

This article presents an innovative procedure for the creation of skip maps in sugarcane fields that combines high-resolution images from a commercial UAV and the OBIA approach to generate useful decision-support data. Based on UAV images, this procedure first performs the identification of the sugarcane crop rows. It then identifies the existent sugarcane within the crop rows and finally, performs skip extraction and the creation of field-extent crop maps.

Section snippets

Study area

The fields used in this study are located near Euclides da Cunha Paulista in São Paulo state, Brazil (22°26′21″S, 52°35′46″W). They have two predominant classes of soils: Rhodic Hapludox (Typic Hapludox) and Quartzarenic Neosol (Typic Quartzipsamment) and they present a moderate slope (<12%) that allows mechanical green harvest. The region has an average ground elevation of approximately 400 m a.s.l. and a humid subtropical climate (Cfa) according to the Köppen classification. The average annual

Global features

The procedure allows the computation of multiple datasets and statistics derived from the outputs, which can help characterize the crop field in terms of crop rows and skip incidence. In addition, the data can be exported in different formats, e.g., vector, raster, or tables, allowing further integration with other data sources. The global features calculated for the study area are presented in Table 1. The dimensions of the field were obtained from the shapefiles, which indicated a total area

Conclusions

The use of UAV images allows the creation of skip maps of sugarcane fields. The method presented in this study proved efficient in the estimation of skip length when compared with information derived in situ. Such information is useful for decision making, agricultural monitoring, and reduction of operational costs reduction, and it can help maintain the longevity and productivity of the crop over successive cycles. The use of UAV technology optimized the surveying of skips in fields,

Acknowledgments

This study was supported by the São Paulo Research Foundation FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) and Odebrecht Agro-Industrial (Process Number. 12/50048-7). The first author was supported by a PhD scholarship from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). We also thank Prof. Mariana Abrantes Giannotti, coordinator of the Geoprocessing Laboratory (LABGEO) of the Polytechnic School of University of São Paulo, for allowing the use of the

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