Robot weed killers - no pain more gain

Weed destruction plays a significant role in crop production, and its automation has both economic and environmental benefits by minimizing the usage of chemicals in the fields. Our aim is to design a small low-cost versatile robot allowing the destruction of weeds that lie between the crop rows by navigating in the field autonomously. Major challenges foreseen are: mapping the unknown geometry of the field, high-level planning of efficient and complete coverage of the field, and controlling the low-level operations of the robot. Traditionally, sensors like odometer have been used for localisation of robots but without much success in real-world scenarios. Specialized sensors like cameras will therefore be investigated and the plethora of image recognition algorithms will be explored and fine-tuned to enable Simultaneous Localisation And Mapping (SLAM) even on resource constrained robotic platforms. Vision-based localisation is not always viable because of the varying weather conditions of the environment and to overcome that, intelligent stochastic data fusion and machine learning algorithms will be utilized to combine data from heterogenous sensor. The image sensors for localisation will be re-used to differentiate crop rows from the weeds, which are cut when they grow. Finally, logics and reinforcement learning techniques will be explored, to exploit the generated map of the field and other sensorial information, to efficiently plan and execute weed elimination

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests

Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package

Terrestrial laser scanning for non-destructive estimates of liana stem biomass

Lianas are important and yet understudied components of tropical forests. Recent studies have shown that lianas are increasing in abundance and biomass in neotropical forests. However, aboveground biomass estimates of lianas are highly uncertain when calculated from allometric relations. This is mainly because of the limited sample size, especially for large lianas, used to construct the allometric models. Furthermore, the allometry of lianas can be weakly constrained mechanically throughout its development from sapling to mature form. In this study, we propose to extract liana stem biomass from terrestrial laser scanning (TLS) data of tropical forests. We show good agreement with a concordance correlation coefficient (CCC) of 0.94 between the TLS-derived volume to reference volume from eleven synthetic lianas. We also compare the TLS-derived biomass for ten real lianas in Nouragues, French Guiana, with the biomass derived from all existing allometric equations for lianas. Our results show relatively low CCC values for all the allometric models with the most commonly used pantropical model overestimating the total biomass by up to 133% compared to the TLS-derived biomass. Our study not only facilitates the testing of allometric equations but also enables non-destructive estimation of liana stem biomass. Since lianas are disturbance-adapted plants, liana abundance is likely to increase with increased forest disturbance. Our method will facilitate the long-term monitoring of liana biomass change in regenerating forests after disturbance, which is critical for developing effective forest management strategies

Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning

Terrestrial laser scanning (TLS) is used to generate realistic 3D tree models that enable a non-destructive way of quantifying tree volume. An accurate value for basic wood density is required to convert tree volume into aboveground biomass (AGB) for forest carbon assessments. However, basic density is characterised by high inter-, intra-species and within-tree variability and a likely source of error in TLS-derived biomass estimates. Here, 31 adult trees of 4 important European timber species (Fagus sylvatica, Larix decidua, Pinus sylvestris, Fraxinus excelsior) were scanned using TLS and then felled for several basic wood density measurements. We derived a reference volume-weighted basic density (ρw) by combining volume from 3D tree models with destructively assessed vertical density profiles. We compared this to basic density retrieved from a single basal disc over bark (ρbd), two perpendicular pith-to-bark increment cores at breast height (ρic), and sourcing the best available local basic wood density from publications. Stump-to-tip trends in basic wood density caused site-average woody AGB estimation biases ranging from −3.3 to + 7.8% when using ρbd and from −4.1 to + 11.8% when using ρic. Basic wood density from publications was in general a bad predictor for ρw as the bias ranged from −3.2 to + 17.2%, with little consistency across different density repositories. Overall, our density-attributed biases were similar to several recently reported biases in TLS-derived tree volume, leading to potentially large compound errors in biomass assessments with TLS if patterns of vertical basic wood density variation are not properly accounted for

Within-site variability of liana wood anatomical traits : a case study in Laussat, French Guiana

Research Highlights: We investigated the variability of vessel diameter distributions within the liana growth form among liana individuals originating from a single site in Laussat, French Guiana. Background and Objectives: Lianas (woody vines) are key components of tropical forests. Lianas are believed to be strong competitors for water, thanks to their presumed efficient vascular systems. However, unlike tropical trees, lianas are overlooked in field data collection. As a result, lianas are often referred to as a homogeneous growth form while little is known about the hydraulic architecture variation among liana individuals. Materials and Methods: We measured several wood hydraulic and structural traits (e.g., basic specific gravity, vessel area, and vessel diameter distribution) of 22 liana individuals in a single sandy site in Laussat, French Guiana. We compared the liana variability of these wood traits and the correlations among them with an existing liana pantropical dataset and two published datasets of trees originating from different, but species-rich, tropical sites. Results: Liana vessel diameter distribution and density were heterogeneous among individuals: there were two orders of magnitude difference between the smallest (4 µm) and the largest (494 µm) vessel diameters, a 50-fold difference existed between extreme vessel densities ranging from 1.8 to 89.3 vessels mm−2, the mean vessel diameter varied between 26 µm and 271 µm, and the individual theoretical stem hydraulic conductivity estimates ranged between 28 and 1041 kg m−1 s−1 MPa−1. Basic specific gravity varied between 0.26 and 0.61. Consequently, liana wood trait variability, even within a small sample, was comparable in magnitude with tree surveys from other tropical sites and the pantropical liana dataset. Conclusions: This study illustrates that even controlling for site and soil type, liana traits are heterogeneous and cannot be considered as a homogeneous growth form. Our results show that the liana hydraulic architecture heterogeneity across and within sites warrants further investigation in order to categorize lianas into functional groups in the same way as trees

Characterising termite mounds in a tropical savanna with UAV laser scanning

Termite mounds are found over vast areas in northern Australia, delivering essential ecosystem services, such as enhancing nutrient cycling and promoting biodiversity. Currently, the detection of termite mounds over large areas requires airborne laser scanning (ALS) or high-resolution satellite data, which lack precise information on termite mound shape and size. For detailed structural measurements, we generally rely on time-consuming field assessments that can only cover a limited area. In this study, we explore if unmanned aerial vehicle (UAV)-based observations can serve as a precise and scalable tool for termite mound detection and morphological characterisation. We collected a unique data set of terrestrial laser scanning (TLS) and UAV laser scanning (UAV-LS) point clouds of a woodland savanna site in Litchfield National Park (Australia). We developed an algorithm that uses several empirical parameters for the semi-automated detection of termite mounds from UAV-LS and used the TLS data set (1 ha) for benchmarking. We detected 81% and 72% of the termite mounds in the high resolution (1800 points m−2) and low resolution (680 points m−2) UAV-LS data, respectively, resulting in an average detection of eight mounds per hectare. Additionally, we successfully extracted information about mound height and volume from the UAV-LS data. The high resolution data set resulted in more accurate estimates; however, there is a trade-off between area and detectability when choosing the required resolution for termite mound detection Our results indicate that UAV-LS data can be rapidly acquired and used to monitor and map termite mounds over relatively large areas with higher spatial detail compared to airborne and spaceborne remote sensing

Moč soustvarjanja osebne znamke predsedniškega kandidata

This paper describes an improved algorithm for segmentation of green vegetation under uncontrolled illumination conditions and also suitable for resource-constrained real-time applications. The proposed algorithm uses a naïve Bayesian model to effectively combine various manually extracted features from two different color spaces namely RGB and HSV. The evaluation of 100 images indicated the better performance of the proposed algorithm than the vegetation index-based methods with comparable execution time. Moreover, the proposed algorithm performed better than the state-of-the-art EASA-based algorithms in terms of processing time and memory usage.AgricultureIsLif

Towards extraction of lianas from terrestrial LiDAR scans of tropical forests

Increased liana abundance results in reduced tree growth and increased tree mortality in tropical forest. The impact of lianas on forest-wide carbon storage has been a special interest for many researchers. The vertical and horizontal spatial distribution of lianas in tropical forest will determine the interaction with trees and the forest carbon cycle. In this study, we will introduce an algorithm to extract lianas from terrestrial laser scanning (TLS) data of a tropical forest. We developed a classification method for separating liana points from other points in a point cloud under canopy. We used a Random Forests machine learning algorithm for the classification of liana points from the other points. The leaf-wood and liana-tree classification accuracies are 90.69% and 94.42%, respectively. The results show the potential of TLS data for analysis the spatial distribution of lianas in forest stands and we explore the potential of extracting lianas from TLS point clouds