Sensors in agriculture and forestry

Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs. Sensors-based technologies provide appropriate tools to achieve the above mentioned goals. The explosive technological advances and development in recent years enormously facilitates the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by the farmers themselves. Precision Agriculture is an emerging area where sensor-based technologies play an important role.RHEA project [42], which is funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986, which has been the platform for the two international conferences on Robotics and associated High-technologies and Equipment mentioned above.Peer Reviewe

Forestry timber typing. Tanana demonstration project, Alaska ASVT

The feasibility of using LANDSAT digital data in conjunction with topographic data to delineate commercial forests by stand size and crown closure in the Tanana River basin of Alaska was tested. A modified clustering approach using two LANDSAT dates to generate an initial forest type classification was then refined with topographic data. To further demonstrate the ability of remotely sensed data in a fire protection planning framework, the timber type data were subsequently integrated with terrain information to generate a fire hazard map of the study area. This map provides valuable assistance in initial attack planning, determining equipment accessibility, and fire growth modeling. The resulting data sets were incorporated into the Alaska Department of Natural Resources geographic information system for subsequent utilization

Monitorización 3D de cultivos y cartografía de malas hierbas mediante vehículos aéreos no tripulados para un uso sostenible de fitosanitarios

En esta Tesis Doctoral se han utilizado las imágenes procedentes de un UAV para abordar la sostenibilidad de la aplicación de productos fitosanitarios mediante la generación de mapas que permitan su aplicación localizada. Se han desarrollado dos formas diferentes y complementarias para lograr este objetivo: 1) la reducción de la aplicación de herbicidas en post-emergencia temprana mediante el diseño de tratamientos dirigidos a las zonas infestadas por malas hierbas en varios cultivos herbáceos; y 2) la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos para el diseño de tratamientos de aplicación localizada de fitosanitarios dirigidos a la parte aérea de los mismos. Para afrontar el control localizado de herbicidas se han estudiado la configuración y las especificaciones técnicas de un UAV y de los sensores embarcados a bordo para su aplicación en la detección temprana de malas hierbas y contribuir a la generación de mapas para un control localizado en tres cultivos herbáceos: maíz, trigo y girasol. A continuación, se evaluaron los índices espectrales más precisos para su uso en la discriminación de suelo desnudo y vegetación (cultivo y malas hierbas) en imágenes-UAV tomadas sobre dichos cultivos en fase temprana. Con el fin de automatizar dicha discriminación se implementó en un entorno OBIA un método de cálculo de umbrales. Finalmente, se desarrolló una metodología OBIA automática y robusta para la discriminación de cultivo, suelo desnudo y malas hierbas en los tres cultivos estudiados, y se evaluó la influencia sobre su funcionamiento de distintos parámetros relacionados con la toma de imágenes UAV (solape, tipo de sensor, altitud de vuelo, momento de programación de los vuelos, entre otros). Por otra parte y para facilitar el diseño de tratamientos fitosanitarios ajustados a las necesidades de los cultivos leñosos se ha desarrollado una metodología OBIA automática y robusta para la caracterización tridimensional (arquitectura y volumen) de cultivos leñosos usando imágenes y modelos digitales de superficies generados a partir de imágenes procedentes de un UAV. Asimismo, se evaluó la influencia de distintos parámetros relacionados con la toma de las imágenes (solape, tipo de sensor, altitud de vuelo) sobre el funcionamiento del algoritmo OBIA diseñado

Automatic classification of grassland herbs in close-range sensed digital colour images

The broad-leaved dock (Rumex obtusifolius L. (RUMOB)) is one of the most harmful and persistent weed species on European grassland and it has been spread into the temperate grassland regions throughout the world. Large dry matter contributions of Rumex obtusifolius L. reduce the quality of the standing forage considerably because of the poor palatability of leaves and tillers and withdraw water and nutrient from surrounding plants. For Central Europe it is estimated that more than 80% of all herbicides used in conventional grassland farming are used to control Rumex species. Until today, herbicides are applied over the whole field, even if Rumex plants are not homogeneously distributed area-wide. Recently developed precision farming techniques based on weed mapping that use mainly image processing, enable site-specific spraying of weeds in arable crops. Until today those techniques have not been applied to grassland weed sensing. Compared to the identification of isolated individual plants on a rather uniform soil background in arable crops, image processing for a more complex environment as grassland requires a different approach. The aim of the thesis was to develop an image processing procedure for automatic detection of grassland weeds using close-range digital colour images, focussing on the detection of RUMOB. A field experiment has been established with grassland plots populated with RUMOB and the other typical broad leaved grassland weeds Taraxacum officinale Web. (TAROF) and Plantago major L. (PLAMA). Digital colour images have been taken from around 1.5 m above ground at three dates in 2005. Image acquisition was done automatically by a vehicle driven on rails alongside to the experimental plots, whereby nearly constant recording geometry conditions were guaranteed. Images were taken during cloud cover in order to avoid direct sunlight. Using the images from 2005 an object-oriented image classification has been developed. Thereby, the leaves of the weeds were separated from the background using parameters of homogeneity and morphology, resulting in a binary image. The remaining image objects in the binary image were contiguous regions of neighbouring pixels related to the object classes of the weed species, soil, and residue objects. Geometrical-, colour and texture features were calculated for each of these objects. Discriminant analysis exhibited that colour and texture features contribute most to the discriminating of objects into the different classes. In a Maximum Likelihood classification these features were used to differentiate the objects into their respective classes. High overall accuracies and even higher RUMOB detection rates were achieved. The algorithm has been modified and applied to images of varying image resolutions. High classification accuracies have been achieved with all image resolutions, whereby the processing time could be improved for images with lowest resolutions. Images were taken at 13 dates over the two grassland growths in 2006. In all the images the plant species were classified automatically using the developed image classification integrated in a graphical user interface software. The coordinates of the objects classified as RUMOB were transformed into Gauss-Krueger system to generate distribution maps of this weed. The combination of object density and area further decreased its misclassifications. RUMOB classification rates across the season were analysed and phenological stages have been identified on which classification performed best. The results demonstrate high potential of machine vision for weed detection in grassland. A classification procedure based on image analysis and Geographic Information System (GIS) post-processing has been developed for detecting Rumex obtusifolius L. and other weeds in grassland with high accuracy. Future projects might focus on the application to real grassland conditions and the derivation of RUMOB distribution maps. Thus, herbicide application maps can be calculated, utilized for site-specific weed control. The development of an image acquisition unit to be mounted on a driving vehicle along with a standardization of image recording is going to be the main focus

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

The influence of pioneer riparian vegetation on river processes from the plant to reach scale

Alluvial rivers have morphologies that are shaped to varying degrees by the character of the riparian vegetation they support. Floodplain vegetation produces bank cohesion, for example, which in turn is responsible for inducing river meandering that gives rise to in-channel bars suitable for pioneer vegetation recruitment. Once established, pioneer vegetation is inundated by channel-forming flows, where it interacts with flow and sediment transport processes. This dissertation quantifies interactions between in-channel pioneer vegetation, which is under-studied relative to floodplain vegetation, and river processes across spatial scales. At the seedling scale, I link field experiments measuring woody riparian seedling uprooting forces to numerical calculations of flow forces. Seedling uprooting sets the trajectory of vegetation-river interactions that may ensue if vegetation survives, becomes established and alters river morphodynamics at the patch, bar, and reach scales. I constrain the differential controls on seedlings’ resisting force, and show that substantial bed scour is required to uproot seedlings. These constraints on seedling uprooting conditions inform management strategies aimed at increasing or decreasing riparian species. I characterize relationships among topographic features created by vegetation patches on river bars and vegetation morphometric parameters. I show that flume-based hydraulic relationships poorly predict field observations. I also demonstrate that the signature of vegetation alters reach-scale morphology. This analysis, combined with one that characterizes the wavelengths of in-channel river topography, shows that vegetation and the topographic features it creates within a channel have a large influence on the distribution of shear stresses compared to other roughness features. Lastly, using a high-resolution hydrodynamic model that accounts for vegetation drag, I simulate the impact of vegetation succession on channel-bend and meander processes by changing the size and density of vegetation on an in-channel bar. A global sensitivity analysis shows that vegetation parameters are nearly as influential as channel characteristics in altering bend hydraulics. For a river reach, simulations show that a vegetated bar changes channel hydraulics and forces in a manner that would be expected to alter channel evolution, and explains qualitative observations of vegetation-mediated river morphologies. This research thus quantifies under which conditions pioneer seedlings can persist and alter channel topography, with implications for changing the morphology of rivers

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun ower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-speci c control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work rstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole eld data spectrum for the classi cation method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of di erent nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of di erent statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great in uence for weed mapping in both sun ower and maize crop

The domestication syndrome in Phoenix dactylifera seeds : toward the identification of wild date palm populations

Investigating crop origins is a priority to understand the evolution of plants under domestication, develop strategies for conservation and valorization of agrobiodiversity and acquire fundamental knowledge for cultivar improvement. The date palm(Phoenix dactylifera L.) belongs to the genus Phoenix, which comprises 14 species morphologically very close, sometimes hardly distinguishable. It has been cultivated for millennia in the Middle East and in North Africa and constitutes the keystone of oasis agriculture. Yet, its origins remain poorly understood as no wild populations are identified. Uncultivated populations have been described but they might represent feral, i.e. formerly cultivated, abandoned forms rather than truly wild populations. In this context, this study based on morphometrics applied to 1625 Phoenix seeds aims to (1) differentiate Phoenix species and (2) depict the domestication syndrome observed in cultivated date palm seeds using other Phoenix species as a "wild" reference. This will help discriminate truly wild from feral forms, thus providing new insights into the evolutionary history of this species. Seed size was evaluated using four parameters: length, width, thickness and dorsal view surface. Seed shape was quantified using outline analyses based on the Elliptic Fourier Transform method. The size and shape of seeds allowed an accurate differentiation of Phoenix species. The cultivated date palm shows distinctive size and shape features, compared to other Phoenix species: seeds are longer and elongated. This morphological shift may be interpreted as a domestication syndrome, resulting from the long-term history of cultivation, selection and human-mediated dispersion. Based on seed attributes, some uncultivated date palms from Oman may be identified as wild. This opens new prospects regarding the possible existence and characterization of relict wild populations and consequently for the understanding of the date palm origins. Finally, we here describe a pipeline for the identification of the domestication syndrome in seeds that could be used in other crops

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Modern Seed Technology

Satisfying the increasing number of consumer demands for high-quality seeds with enhanced performance is one of the most imperative challenges of modern agriculture. In this view, it is essential to remember that the seed quality of crops does not improve