Precision agriculture trends in fruit growing from 2016 to 2020.

Brazilian fruit culture has a great influence on the social and economic sector in the most diverse regions of the country, generating employment and income in the exercise of its activities. As it is an activity carried out most often in a manual and conventional manner, fruit culture has a great potential for technological growth, especially when adopting the concepts applied by precision agriculture on the crops of grains, fibers and energy, creating a new segment, Precision Fruit Farming. The present work aims to carry out a bibliographic review on the main trends that have emerged in the last five years on Precision Fruit growing, highlighting its future perspectives and the history of technological evolution. 83 articles were analyzed, classified in different perennial cultures and applications, such as machine learning, remote sensing, robotics, using UAV to obtain different vegetation indexes, among others. Index Terms: Machine Learning; Vegetation Indexes; Robotics; Tendências da agricultura de precisão em fruticultura no período de 2016 A 2020 Resumo - A fruticultura brasileira exerce grande influência sobre o setor social e econômico nas mais diversas regiões do País, gerando emprego e renda no exercício de suas atividades. Por se tratar de uma atividade realizada, na maioria das vezes, de forma manual e convencional, a fruticultura possui grande potencial de crescimento tecnológico, principalmente ao adotar os conceitos aplicados pela agricultura de precisão sobre as culturas de grãos, fibras e energia, criando um novo segmento, a Fruticultura de Precisão. O presente estudo objetivou realizar uma revisão bibliográfica sobre as principais tendências que surgiram nos últimos cinco anos sobre a Fruticultura de Precisão, destacando suas perspectivas futuras e o histórico de evolução tecnológica. Foram analisados 83 artigos, classificados em diferentes culturas perenes e aplicações, como machine learning (aprendizado de máquinas), sensoriamento remoto, robótica, utilização de VANT para obtenção de diferentes índices de vegetação, entre outras. Termos para indexação: Aprendizagem de Máquina; Índices de Vegetação; Robótica; Sensoriamento Remoto; VANT

Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 > 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 < R2 < 0.9) led to satisfactory high and consistent accuracies for all months.status: Published onlin

Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 &gt; 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 &lt; R2 &lt; 0.9) led to satisfactory high and consistent accuracies for all months

Modelling seagrass blue carbon stock in seagrass-mangrove habitats using remote sensing approach

Modelling seagrass blue carbon stocks are essential to complement the satellitebased remote sensing in detecting the underground seagrass carbon stocks. The green carbon initiatives have for long reported the detailed mapping and estimation procedural as well as the audit protocol of the global terrestrial carbon stocks. Research on the blue carbon mapping and its related modelling and estimation, on the other hand, is rarely if ever published as part of its importance is realised but remained scattered. Therefore, this study aimed at investigating blue carbon stocks in seagrass habitats by estimating the total carbon stored in seagrass using the satellite-based technique. The specific objectives are to : 1) assess and adapt some selected models for deriving seagrass total above-ground carbon (STAGC); 2) formulate new approach based-on selected models to combine with in-situ data, to model and estimate blue carbon stocks from seagrass total below-ground carbon (STBGC); 3) develop a novel technique using the selected models with soil organic carbon (SOC) to model and estimate the blue carbon stocks from seagrass total soil organic carbon (STSOC); and 4) integrate all the models (STAGC, STBGC, and STSOC) to produce a framework for the mapping and estimation of seagrass total blue carbon stock (STBCS). Suitable logistic functions were selected and applied on the satellite images to investigate seagrass, and soil carbon stocks along the seagrass meadows of Peninsular Malaysia (PM) coastline All the Landsat ETM+’s shortwave visible bands (blue, green, red) were employed for detecting and mapping seagrass stocks boundary within the coastline of PM. The derivation of STAGC was adopted from the existing bottom reflectance index (BRI) based technique via establishing a strong relationship between BRI with seagrass total aboveground biomass (STAGB). While for STBGC estimation, the STAGB^ (STAGB obtained from BRI image) were correlated with seagrass total below-ground biomass derived from insitu measurement (STBGB^^ro). Both these STAGB^ and STBGB^.^ro were converted into STAGC and STBGC using a conversion factor. Furthermore, the derivation of seagrass total soil organic carbon derived via laboratory test (STSOCi^b) was achieved through correlating BRI values with corresponding in-situ samples of soil organic carbon (SOC) obtained from the laboratory analysis by the Carbon-Hydrogen Nitrogen Sulphur (CHNS) analyser. These models were generated from the three major sample areas (Johor, Penang, and Terengganu), which were used to estimate the entire seagrass carbon stocks in the coastline of PM. The models revealed a robust correlation results for BRI versus STAGB (R2 = 0.962, p< 0.001), STAGB^, versus STBGB/A,wro (R2 = 0.933, p< 0.001,), and BRI and STSOC (R2 = 0 .989, p< 0.001) respectively. The STBCS for the whole seagrass meadows along the coastline of PM was finally realised, demonstrating a good agreement in accuracy assessment (Root Mean Square Error (RMSE) = +- <1MtC/ha\). It is, therefore, concluded that the new approach introduced by this research on STBGC and STSOC estimation was tested and proved significant on the entire STBCS quantification for the PM coastline. The contributions are critical to fast-track the United Nations Framework Convention on Climate Change (UNFCCC) agreement to report the STBCS contents. Hence, this study has managed to propose a new fundamental initiative for estimating STBCS for speedy realisation of 2020 agenda on targets 14.2 and 14.5 of United Nations’ Sustainable Development Goal 14th (life below the water)

Optické vlastnosti listu ve vztahu k anatomickým vlastnostem listu

K předpovědi reakcí ekosystémů na faktory prostředí se běžně používají funkční znaky rostlin na úrovni listu, popisující projevy globálních změn klimatu na úrovni ekosystémů. Mezi funkční znaky rostlin řadíme jak biofyzikální vlastnosti listu (např. obsah fotosyntetických pigmentů a obsahu vody) tak jeho strukturní vlastnosti (např. tloušťka listu a poměr fotosyntetických a nefotosyntetických pletiv listu). Biofyzikální a strukturní vlastnosti listu je možné zjišťovat buď destruktivně v laboratoři, nebo nedestruktivně s využitím optických vlastností listu. Ačkoli je odhadování obsahu chlorofylu na základě optických vlastností listů dobře zavedenou metodou, vliv struktury a vnitřní anatomie listů na jejich optické vlastnosti je důkladně studován teprve v posledních dvou dekádách. Publikace zahrnuté v mé práci a většina práce je věnována evropským opadavým dřevinám, typickým pro temperátní a hemiboreální lesy s listy vykazujícími podobnou dorziventrální strukturu, (tj. mezofyl je diferencován na palisádový a houbovitý parenchym). Dále má disertační práce zahrnuje studii vlivu strukturních znaků povrchu listů dvou skupin bylin na jejich optické vlastnosti. V této studii byly použity dvě skupiny fylogeneticky blízkých bylin se srovnatelnou vnitřní strukturou listů (mutanty Arabidopsis thaliana L. a...Plant functional traits at the leaf level are commonly used to predict ecosystem responses to environmental factors and describe global climate change processes at the ecosystem level. Plant functional traits include both leaf biophysical traits (e.g., photosynthetic pigment content and water content) and structural traits (e.g., leaf thickness and proportion of photosynthetic and non-photosynthetic tissues). Leaf biophysical and structural traits can be detected either destructively in the laboratory or non-destructively using leaf optical properties. Although estimating chlorophyll content from leaf optical properties is a well-established methodology, the influence of leaf structure and internal anatomy on leaf optical properties has only been thoroughly studied in the last two decades. The papers included in my thesis and my thesis itself are mostly focused on the study of typical European deciduous trees of temperate and hemiboreal forests with leaves having a dorsiventral structure (i.e., the mesophyll is differentiated into palisade and spongy parenchyma). Furthermore, my thesis includes a study on the effect of leaf surface structural traits on optical properties. In this study, two groups of phylogenetically close herbs with comparable internal leaf structure were used (mutants of...Department of Experimental Plant BiologyKatedra experimentální biologie rostlinFaculty of SciencePřírodovědecká fakult

Novel spectral imaging instrumentation for environmental sensing in extreme environments

Spectral imaging techniques provide a valuable means of improving our understanding of the world around us. Environmental monitoring approaches that utilise these techniques are, therefore, essential to our understanding of the effects of climate change. Hyperspectral imaging applications are of particular benefit to a broad range of environmental monitoring scenarios, providing rich datasets that combine both spectral and spatial information, enabling intricate features and variations to be visualised. However, to date, most commercially available hyperspectral instrumentation remains bulky and expensive, significantly limiting their user-base and accessibility. These factors substantially limit the use of these instruments resulting in much of our information coming from a few well-resourced research teams across a limited number of more easily accessed field locations. These limitations, have a compounded effect on the quality and robustness of hyperspectral data outputs, particularly within more extreme settings, as the comparatively small sample of more accessible locations is not necessarily representative of the much larger whole. This thesis presents on the development and testing of three novel low-cost hyperspectral imaging instruments designed specifically for environmental monitoring applications, providing valuable, low-cost alternatives to currently available commercial systems. Specifically, the three instruments presented within this thesis are: a low-cost laboratory-based hyperspectral imager, a semi-portable instrument capable of accurate data capture within a laboratory setting; the Hyperspectral Smartphone, an ultra-low-cost smartphone-based fully portable hyperspectral imager; and a low-cost high-resolution hyperspectral imager capable of resolving mm-scale spatial targets. All instruments were rigorously tested to analyse and evaluate their performances. Each instrument was shown to perform well across a range of environmental monitoring applications demonstrating that expensive commercial instrumentation is not required to achieve accurate and robust hyperspectral imaging. These low-cost instruments could promote the widespread dissemination of accessible hyperspectral imaging equipment, facilitating the democratisation of hyperspectral measurement modalities across environmental monitoring applications and beyond