Precision Agriculture Applications in a High Density Olive Grove Adapted for Mechanical Harvesting in Greece

Gertsis, Athanasios; Fountas, Dimitrios Spyros; Arpasanu, Ionel; Michaloudis, Masianos

doi:10.1016/j.protcy.2013.11.021

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…In addition, some spectral bands have been combined to estimate some VIs [46,47]. Firstly, the sharp contrast between soil and leaf reflectance in the NIR and red bands is typically used to calculate the Normalized Difference Vegetation Index (NDVI) [48]. It is a well-known spectral index applied in remote sensing.…”

Section: Algorithm 1: Reflectance Map Computingmentioning

confidence: 99%

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

et al. 2020

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3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

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Section: Algorithm 1: Reflectance Map Computingmentioning

confidence: 99%

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

et al. 2020

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“…In addition, the Red and NIR bands were combined to calculate the Normalized Difference Vegetation Index (NDVI). It is a well-known spectral index applied in remote sensing to assess the green biomass, leaf cover and chlorophyll per unit ground area [26,27].…”

Section: Uav-based and Field Datamentioning

confidence: 99%

The Impact of Canopy Reflectance on the 3D Structure of Individual Trees in a Mediterranean Forest

et al. 2020

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The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure. A heterogeneous natural environment in a Mediterranean forest, in which various tree species (pine, oak and eucalyptus) coexist, was covered using a high-resolution digital camera and a multispectral sensor. These devices were mounted on an Unmanned Aerial Vehicle (UAV) in order to observe the tree architecture and the spectral reflectance at the same time. The Structure from Motion (SfM) method was applied to model the 3D structures using RGB images from the high-resolution camera. The geometric accuracy of the resulting point cloud was validated by georeferencing the study area through multiple ground control points (GCPs). Then, the point cloud was enriched with the reflected light in four narrow-bands (green, near-infrared, red and red-edge). Furthermore, the Normalized Difference Vegetation Index (NDVI) was calculated in order to measure the tree vigor. A comprehensive analysis based on structural and spectral features of individual trees was proposed. A spatial segmentation was developed to detect single-trees in a forest and for each one to identify the crown and trunk. Consequently, structural parameters were extracted, such as the tree height, the diameter at breast height (DBH) and the crown volume. The validation of these measurements was performed by field data, which were taken using a Total Station (TS). In addition, these characteristics were correlated with the mean reflectance in the tree canopy. Regarding the observed tree species, a statistical analysis was carried out to study the impact of reflectance on the 3D tree structure. By applying our method, a more detailed knowledge of forest dynamics can be gained and the impact of available solar irradiance on single-trees can be analyzed.

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“…Many harvesting methods exist and many variables are applied. The choice of a method instead of another one depends on many factors such as: age of the plants, shape/profile of the ground, the size of the olive tree orchard, shape and size of the canopy, concurrent or gradual fruit ripening, cost of the workmanship, cost of machineries (Ferguson, 2006;Ferguson et al, 2010;Gambella, 2013;Gertsis et al, 2013;Deboli et al, 2014;Famiani et al, 2014;Almeida et al, 2015;Jimenez-Jimenez et al, 2015). Here we describe the main techniques used for this purpose.…”

Section: Fruit Picking/harvesting Systemsmentioning

confidence: 99%

Pre-and post-harvest factors and their impact on oil composition and quality of olive fruit

Mele

Giuffré

2018

Emir J Food Agric

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Olive fruit and its oil are in great demand due to its nutritional value, which can be influenced by the pre-and post-harvest factors. The pre-and post-harvest factors are discussed in this article in an effort to recognize as being and investigate the ultimate effect of these factors on the olive fruit and its oil composition. Genetic, agronomic and environmental factors influence olive fruits and olive oil composition. The olive cultivar influences fruit weight, olive oil content, fatty acids, peroxide value, esters, volatile compounds and fatty alcohol content. Chlorophyll content, carotenoids, flavor and volatile compounds vary depending upon geographical area of origin. Environmental conditions have an effect on enzymes, oil composition, fatty acids, microbial activity, esters, flowering, growth and development, ripening rate, insect activity and susceptibility or resistance to diseases. Tree age has an impact on phenol content of olive oil. The boron, nitrogen, potassium, salinity and zinc treatments have an influence on the olive fruit and its oil composition. In addition, the ripening degree, harvesting time has enforcement on olive oil content. Leaf removal and washing of the fruit promote the purity of the olive oil for both chemical and sensorial aspects. A not prolonged fruit crushing together with a not prolonged paste malaxation at a temperature below 27 °C increase the sensory and physicochemical qualities of olive oil. Inappropriate oil extraction promotes the off-ﬂavors in the olive oil. Dark storage helps to retain the chemical and sensorial olive oil quality. The oil quality degrades toward the inside of cooking temperature.

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Precision Agriculture Applications in a High Density Olive Grove Adapted for Mechanical Harvesting in Greece

Cited by 8 publications

References 3 publications

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

The Impact of Canopy Reflectance on the 3D Structure of Individual Trees in a Mediterranean Forest

Pre-and post-harvest factors and their impact on oil composition and quality of olive fruit

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