This page has only limited features, please log in for full access.
Soil erosion is a severe and complex issue in the agriculture area. The main objective of this study was to assess the soil loss in two regions, testing different methodologies and combining different factors of the Revised Universal Soil Loss Equation (RUSLE) based on Geographical Information Systems (GIS). To provide the methodologies to other users, a GIS open-source application was developed. The RUSLE equation was applied with the variation of some factors that compose it, namely the slope length and slope steepness (LS) factor and practices factor (P), but also with the use of different sources of information. Eight different erosion models (M1 to M8) were applied to the two regions with different ecological conditions: Montalegre (rainy-mountainous) and Alentejo (dry-flat), both in Portugal, to compare them and to evaluate the soil loss for 3 potential erosion levels: 0–25, 25–50 and >50 ton/ha·year. Regarding the methodologies, in both regions the behavior is similar, indicating that the M5 and M6 methodologies can be more conservative than the others (M1, M2, M3, M4 and M8), which present very consistent values in all classes of soil loss and for both regions. All methodologies were implemented in a GIS application, which is free and available under QGIS software.
Lia Duarte; Mário Cunha; Ana Teodoro. Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal. Land 2021, 10, 554 .
AMA StyleLia Duarte, Mário Cunha, Ana Teodoro. Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal. Land. 2021; 10 (6):554.
Chicago/Turabian StyleLia Duarte; Mário Cunha; Ana Teodoro. 2021. "Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal." Land 10, no. 6: 554.
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of
Sandro Magalhães; Luís Castro; Germano Moreira; Filipe dos Santos; Mário Cunha; Jorge Dias; António Moreira. Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse. Sensors 2021, 21, 3569 .
AMA StyleSandro Magalhães, Luís Castro, Germano Moreira, Filipe dos Santos, Mário Cunha, Jorge Dias, António Moreira. Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse. Sensors. 2021; 21 (10):3569.
Chicago/Turabian StyleSandro Magalhães; Luís Castro; Germano Moreira; Filipe dos Santos; Mário Cunha; Jorge Dias; António Moreira. 2021. "Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse." Sensors 21, no. 10: 3569.
With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil’s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R2) of humidity (R2 = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R2 = 0.642, p < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R2 grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees’ growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken.
Yahui Guo; Shouzhi Chen; Zhaofei Wu; Shuxin Wang; Christopher Robin Bryant; Jayavelu Senthilnath; Mario Cunha; Yongshuo Fu. Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform. Remote Sensing 2021, 13, 1795 .
AMA StyleYahui Guo, Shouzhi Chen, Zhaofei Wu, Shuxin Wang, Christopher Robin Bryant, Jayavelu Senthilnath, Mario Cunha, Yongshuo Fu. Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform. Remote Sensing. 2021; 13 (9):1795.
Chicago/Turabian StyleYahui Guo; Shouzhi Chen; Zhaofei Wu; Shuxin Wang; Christopher Robin Bryant; Jayavelu Senthilnath; Mario Cunha; Yongshuo Fu. 2021. "Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform." Remote Sensing 13, no. 9: 1795.
Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen’s |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity.
Bruno Marcos; João Gonçalves; Domingo Alcaraz-Segura; Mário Cunha; João Honrado. A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes. Remote Sensing 2021, 13, 780 .
AMA StyleBruno Marcos, João Gonçalves, Domingo Alcaraz-Segura, Mário Cunha, João Honrado. A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes. Remote Sensing. 2021; 13 (4):780.
Chicago/Turabian StyleBruno Marcos; João Gonçalves; Domingo Alcaraz-Segura; Mário Cunha; João Honrado. 2021. "A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes." Remote Sensing 13, no. 4: 780.
Predawn leaf water potential (Ψpd) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy’s water status. Spectral data methods have been applied to monitor and assess crop’s biophysical variables. This work developed two models to estimate Ψpd using a hand-held spectroradiometer (400−1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Ψpd. Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Ψpd in a commercial vineyard in the Douro Wine Region. The first approach estimated Ψpd through vine’s canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVIopt1_950;596;521; SPVIopt2_896;880;901; PRI_CI2opt_539;560,573;716 and NPCIopt_983;972, as well as a time-dynamic variable based on Ψpd (Ψpd_0). The second modelling approach is based on pigments’ concentrations; several VIs were optimized for non-correlated pigments of vine’s leaves, assessed by its hyperspectral reflectance. The following variables for Ψpd estimation were selected through stepwise forward method: Ψpd_0; NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13–14%.
Renan Tosin; Isabel Pôças; Helena Novo; Jorge Teixeira; Natacha Fontes; António Graça; Mario Cunha. Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region. Scientia Horticulturae 2020, 278, 109860 .
AMA StyleRenan Tosin, Isabel Pôças, Helena Novo, Jorge Teixeira, Natacha Fontes, António Graça, Mario Cunha. Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region. Scientia Horticulturae. 2020; 278 ():109860.
Chicago/Turabian StyleRenan Tosin; Isabel Pôças; Helena Novo; Jorge Teixeira; Natacha Fontes; António Graça; Mario Cunha. 2020. "Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region." Scientia Horticulturae 278, no. : 109860.
Roads and roadsides provide dispersal channels for non-native invasive alien plants (IAP), many of which hold devastating impacts in the economy, human health, biodiversity and ecosystem functionality. Remote sensing is an essential tool for efficiently assessing and monitoring the dynamics of IAP along roads. In this study, we explore the potentialities of object based image analysis (OBIA) approach to map several invasive plant species along roads using very high spatial resolution imagery. We compared the performance of OBIA approaches implemented in one open source software (OTB/Monteverdi) against those available in two proprietary programs (eCognition and ArcGIS). We analysed the images by two sequential processes. First, we obtained a land-cover map for 15 study sites by segmenting the images with the algorithms Mean Shift Segmentation (MSS) and Multiresolution Segmentation (MRS), and by classifying the segmented images with the algorithms Support Vector Machine (SVM), Nearest Neighbour Classifier (NNC) and Maximum Likelihood Classifier (MLC). We created a mask using the polygons classified as non-vegetation to crop the images of the 15 study sites. Second, we repeated the previous segmentation and classification steps over the 15 masked images of vegetated areas using the same algorithms. OTB/Monteverdi, with MSS and SVM algorithms, showed to be a good software for land-cover mapping (OA = 87.0%), as well as ArcGIS, with MSS and MLC algorithms (OA = 84.3%). However, these two programs, using the same segmentation algorithms, did not achieve good accuracy results when mapping IAP species (OAOTB/Monteverdi = 63.3%; OAArcGIS = 45.7%). eCognition, with MRS and NNC algorithms, reached better classification results in both land-cover and IAP maps (OALand-cover = 95.7%; OAInvasive-plant = 92.8%). ’Bare soil’ and ‘Road’, and ‘A. donax’ were the classes with best and worst overall accuracy, respectively, when mapping land-cover classes in the three programs. ‘Other trees’ was the class with the most accurate and significant differences in the three programs when mapping IAP species. The separation of each invasive species should be improved with a phenology-based design of field surveys. This study demonstrates the effectiveness of sequential segmentation and classification of RS data for mapping and monitoring plant invasions along linear infrastructures, which allows to reduce the time, cost and hazard of extensive field campaigns along roadsides.
P. Lourenço; A.C. Teodoro; J.A. Gonçalves; J.P. Honrado; M. Cunha; N. Sillero. Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data. International Journal of Applied Earth Observation and Geoinformation 2020, 95, 102263 .
AMA StyleP. Lourenço, A.C. Teodoro, J.A. Gonçalves, J.P. Honrado, M. Cunha, N. Sillero. Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 2020; 95 ():102263.
Chicago/Turabian StyleP. Lourenço; A.C. Teodoro; J.A. Gonçalves; J.P. Honrado; M. Cunha; N. Sillero. 2020. "Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data." International Journal of Applied Earth Observation and Geoinformation 95, no. : 102263.
Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed.
Jorge Mendes; Tatiana M. Pinho; Filipe Neves Dos Santos; Joaquim J. Sousa; Emanuel Peres; José Boaventura-Cunha; Mário Cunha; Raul Morais. Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review. Agronomy 2020, 10, 855 .
AMA StyleJorge Mendes, Tatiana M. Pinho, Filipe Neves Dos Santos, Joaquim J. Sousa, Emanuel Peres, José Boaventura-Cunha, Mário Cunha, Raul Morais. Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review. Agronomy. 2020; 10 (6):855.
Chicago/Turabian StyleJorge Mendes; Tatiana M. Pinho; Filipe Neves Dos Santos; Joaquim J. Sousa; Emanuel Peres; José Boaventura-Cunha; Mário Cunha; Raul Morais. 2020. "Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review." Agronomy 10, no. 6: 855.
The impact of climate on wine production (WP) temporal cycles in Douro (DR) and Vinhos Verdes (VVR) wine regions for a period of about 80 years, characterized by strong technological trend and climate variability, was modelled. The cyclical properties of WP, and which cycles are determined by spring temperature (ST) and soil water during summer (SW), were identified. It was achieved by applying a time-frequency approach, which is based on Kalman filter in the time domain. The time-varying autoregressive model can explain more than 67% (DR) and 95% (VVR) of the WP’ variability and the integration of the ST and mainly SW increase the models’ reliability. The results were then transferred into the frequency domain, and can show that WP in both regions is characterized by two cycles close to 5-6 and 2.5 years around the long run trend. The ST and SW showed great capacity to explain the cyclicality of WP in the studied regions being the coherence temporarily much more stable in VVR than in the DR, where a shift of the relative importance away from ST to SW can be recognized. This could be an indicator of lower impact of the foreseen hot and dry climate scenarios on WP in the regions with a maritime climate, such as the VVR, compared with hot and dry wine regions. Despite the marked differences in the two studied regions on ecological, viticulture practices and technological trend, the modelling approach based on time-frequency proved to be an efficient tool to infer the impact of climate on the dynamics of cyclical properties of regional WP, foreseeing its generalized use in other regions. This modelling approach can be an important tool for planning in the wine industry as well as for mitigation strategies facing the scenarios that combine technological progress and climate change.
Mario Cunha; Christian Richter. Climate-induced cyclical properties of regional wine production using a time-frequency approach in Douro and Minho Wine Regions. Ciência e Técnica Vitivinícola 2020, 35, 16 -29.
AMA StyleMario Cunha, Christian Richter. Climate-induced cyclical properties of regional wine production using a time-frequency approach in Douro and Minho Wine Regions. Ciência e Técnica Vitivinícola. 2020; 35 (1):16-29.
Chicago/Turabian StyleMario Cunha; Christian Richter. 2020. "Climate-induced cyclical properties of regional wine production using a time-frequency approach in Douro and Minho Wine Regions." Ciência e Técnica Vitivinícola 35, no. 1: 16-29.
Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.
Sosdito Mananze; Isabel Pôças; Mário Cunha. Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique. Remote Sensing 2020, 12, 1279 .
AMA StyleSosdito Mananze, Isabel Pôças, Mário Cunha. Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique. Remote Sensing. 2020; 12 (8):1279.
Chicago/Turabian StyleSosdito Mananze; Isabel Pôças; Mário Cunha. 2020. "Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique." Remote Sensing 12, no. 8: 1279.
The advances achieved during the last 30 years demonstrate the aptitude of the remote sensing-based vegetation indices (VI) for the assessment of crop evapotranspiration (ETc) and irrigation requirements in a simple, robust and operative manner. The foundation of these methodologies is the well-established relationship between the VIs and the basal crop coefficient (Kcb), resulting from the ability of VIs to measure the radiation absorbed by the vegetation, as the main driver of the evapotranspiration process. In addition, VIs have been related with single crop coefficient (Kc), assuming constant rates of soil evaporation. The direct relationship between VIs and ET is conceptually incorrect due to the effect of the atmospheric demand on this relationship. The rising number of Earth Observation Satellites potentiates a data increase to feed the VI-based methodologies for estimating and mapping either the Kc or Kcb, with improved temporal coverage and spatial resolution. The development of operative platforms, including satellite constellations like Sentinels and drones, usable for the assessment of Kcb through VIs, opens new possibilities and challenges. This work analyzes some of the questions that remain inconclusive at scientific and operational level, including: (i) the diversity of the Kcb-VI relationships defined for different crops, (ii) the integration of Kcb-VI relationships in more complex models such as soil water balance, and (iii) the operational application of Kcb-VI relationships using virtual constellations of space and aerial platforms that allow combining data from two or more sensors.
I. Pôças; A. Calera; I. Campos; Mario Cunha. Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches. Agricultural Water Management 2020, 233, 106081 .
AMA StyleI. Pôças, A. Calera, I. Campos, Mario Cunha. Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches. Agricultural Water Management. 2020; 233 ():106081.
Chicago/Turabian StyleI. Pôças; A. Calera; I. Campos; Mario Cunha. 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches." Agricultural Water Management 233, no. : 106081.
The predawn leaf water potential (ѱpd) is an eco-physiological indicator widely used for assessing vines water status and thus supporting irrigation management in several wine regions worldwide. However, the ѱpd is measured in a short time period before sunrise and the collection of a large sample of points is necessary to adequately represent a vineyard, which constitute operational constraints. In the present study, an alternative method based on hyperspectral data derived from a handheld spectroradiometer and machine learning algorithms was tested and validated for assessing grapevine water status. Two test sites in Douro wine region, integrating three grapevine cultivars, were studied for the years of 2014, 2015, and 2017. Four machine learning regression algorithms were tested for predicting the ѱpd as a continuous variable, namely Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR). Three predicting variables, including two vegetation indices (NRI554,561 and WI900,970) and a time-dynamic variable based on the ѱpd (ѱpd_0), were applied for modelling the response variable (ѱpd). Additionally, the predicted values of ѱpd were aggregated into three classes representing different levels of water deficit (low, moderate, and high) and compared with the corresponding classes of ѱpd observed values. A root mean square error (RMSE) and a mean absolute error (MAE) lower or equal than 0.15 MPa and 0.12 MPa, respectively, were obtained with an external validation data set (n = 71 observations) for the various algorithms. When the modelling results were assessed through classes of values, a high overall accuracy was obtained for all the algorithms (82–83%), with prediction accuracy by class ranging between 79% and 100%. These results show a good performance of the predictive models, which considered a large variability of climatic, environmental, and agronomic conditions, and included various grape cultivars. By predicting both continuous values of ѱpd and classes of ѱpd, the approach presented in this study allowed obtaining 2-levels of accurate information about vines water status, which can be used to feed management decisions of different types of stakeholders.
Isabel Pôças; Renan Tosin; Igor Gonçalves; Mario Cunha. Toward a generalized predictive model of grapevine water status in Douro region from hyperspectral data. Agricultural and Forest Meteorology 2019, 280, 107793 .
AMA StyleIsabel Pôças, Renan Tosin, Igor Gonçalves, Mario Cunha. Toward a generalized predictive model of grapevine water status in Douro region from hyperspectral data. Agricultural and Forest Meteorology. 2019; 280 ():107793.
Chicago/Turabian StyleIsabel Pôças; Renan Tosin; Igor Gonçalves; Mario Cunha. 2019. "Toward a generalized predictive model of grapevine water status in Douro region from hyperspectral data." Agricultural and Forest Meteorology 280, no. : 107793.
Soil erosion constitute a major threat to human lives and assets worldwide, as well as a major environmental disturbance. The Revised Universal Soil Loss Equation (RUSLE) integrated with Geographical Information System (GIS) has been the most widely used model in predicting and mapping soil erosion loss. Remote sensing has particular utility for soil loss model applications, providing observations on several key aspects of Land use and Land cover (LULC) linked to the cover-management factor C of the RUSLE, over wide areas and in consistent and repeatable measurements. A free and open source GIS application coupled with remote sensing data was developed under QGIS software allowing to improve the C factor functionality: (i) automatically download satellite images; (ii) clip with the study case and; (ii) perform a supervised or unsupervised classification, in order to obtain the land cover classification and produce the final C map. One of the most efficient supervised classification algorithms is the Support Vector Machine (SVM). Random Forest (RF) is also an easy-to-use machine learning algorithm for supervised classification. The automation of this functionality was based in the R and SAGA software, both integrated in QGIS. To perform the supervised classification, SVM and RF methods were incorporated. The overall accuracy and Kappa values are also automatically obtained by the R script and GRASS algorithms, which allows to evaluate the result obtained. To perform the unsupervised classification K-means algorithm from SAGA was used. This updating in RUSLE application improve the results obtained for C factor and help us to obtain a most accurate estimation of RUSLE erosion risk map. The application was tested using Sentinel 2A images in two different periods, after and before the forest fire event in Coimbra region, Portugal. In the end, the three resulted maps from SVM, RF and K-means classification were compared.
Lia Duarte; Ana Teodoro; Mario Cunha. A semi-automatic approach to derive land cover classification in soil loss models. Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 111560B .
AMA StyleLia Duarte, Ana Teodoro, Mario Cunha. A semi-automatic approach to derive land cover classification in soil loss models. Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():111560B.
Chicago/Turabian StyleLia Duarte; Ana Teodoro; Mario Cunha. 2019. "A semi-automatic approach to derive land cover classification in soil loss models." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 111560B.
Manisha Sirsat; João Mendes-Moreira; Carlos Ferreira; Mario Cunha. Machine Learning predictive model of grapevine yield based on agroclimatic patterns. Engineering in Agriculture, Environment and Food 2019, 12, 443 -450.
AMA StyleManisha Sirsat, João Mendes-Moreira, Carlos Ferreira, Mario Cunha. Machine Learning predictive model of grapevine yield based on agroclimatic patterns. Engineering in Agriculture, Environment and Food. 2019; 12 (4):443-450.
Chicago/Turabian StyleManisha Sirsat; João Mendes-Moreira; Carlos Ferreira; Mario Cunha. 2019. "Machine Learning predictive model of grapevine yield based on agroclimatic patterns." Engineering in Agriculture, Environment and Food 12, no. 4: 443-450.
Viticulturists need to obtain the estimation of productivity map during the grape vine harvesting, to understand in detail the vineyard variability. An accurate productivity map will support the farmer to take more informed and accurate intervention in the vineyard in line with the precision viticulture concept. This work presents a novel solution to measure the productivity during vineyard harvesting operation realized by a grape harvesting machine. We propose 2D LIDAR sensor attached to low cost IoT module located inside the harvesting machine, to estimate the volume of grapes. Besides, it is proposed data methodology to process data collected and productivity map, considering GIS software, expecting to support the winemakers decisions. A PCD map is also used to validate the method developed by comparison.
Pedro Moura; Daniela Ribeiro; Filipe Neves dos Santos; Alberto Gomes; Ricardo Baptista; Mario Cunha. Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor. Algorithms and Data Structures 2019, 121 -133.
AMA StylePedro Moura, Daniela Ribeiro, Filipe Neves dos Santos, Alberto Gomes, Ricardo Baptista, Mario Cunha. Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor. Algorithms and Data Structures. 2019; ():121-133.
Chicago/Turabian StylePedro Moura; Daniela Ribeiro; Filipe Neves dos Santos; Alberto Gomes; Ricardo Baptista; Mario Cunha. 2019. "Estimation of Vineyard Productivity Map Considering a Cost-Effective LIDAR-Based Sensor." Algorithms and Data Structures , no. : 121-133.
The dynamic effects of kaolin clay particle film application on the temperature and spectral reflectance of leaves of two autochthonous cultivars (Touriga Nacional (TN, n=32) and Touriga Franca (TF, n=24)) were studied in the Douro wine region. The study was implemented in 2017, in conditions prone to multiple environmental stresses that include excessive light and temperature as well as water shortage. Light reflectance from kaolin-sprayed leaves was higher than the control (leaves without kaolin) on all dates. Kaolin’s protective effect over leaves’ temperatures was low on the 20 days after application and ceased about 60 days after its application. Differences between leaves with and without kaolin were explained by the normalized maximum leaf temperature (T_max_f_N), reflectance at 400 nm, 532 nm, and 737 nm, as assessed through TN data. The wavelengths of 532 nm and 737 nm are associated with plant physiological processes, which support the selection of these variables for assessing kaolin’s effects on leaves. The application of principal component analysis to the TF data, based on these four variables (T_max_f_N and reflectances: 400, 532, 737 nm) selected for TN, explained 83.56% of data variability (considering two principal components), obtaining a clear differentiation between leaves with and without kaolin. The T_max_f_N and the reflectance at 532 nm were the variables with a greater contribution for explaining data variability. The results improve the understanding of the vines’ response to kaolin throughout the grapevine cycle and support decisions about the re-application timing.
Renan Tosin; Isabel Pôças; Mario Cunha. Spectral and thermal data as a proxy for leaf protective energy dissipation under kaolin application in grapevine cultivars. Open Agriculture 2019, 4, 294 -304.
AMA StyleRenan Tosin, Isabel Pôças, Mario Cunha. Spectral and thermal data as a proxy for leaf protective energy dissipation under kaolin application in grapevine cultivars. Open Agriculture. 2019; 4 (1):294-304.
Chicago/Turabian StyleRenan Tosin; Isabel Pôças; Mario Cunha. 2019. "Spectral and thermal data as a proxy for leaf protective energy dissipation under kaolin application in grapevine cultivars." Open Agriculture 4, no. 1: 294-304.
Mechanisation is a key input in modern agriculture, while it accounts for a large part of crop production costs, it can bring considerable farm benefits if well managed. Models for simulated machinery costs, may not replace actual cost measurements but the information obtained through them can replace a farm’s existing records, becoming more valuable to decision makers. MACHoice, a decision support system (DSS) presented in this paper, is a farm machinery cost estimator and break-even analyzer of alternatives for agricultural operations, developed using user-driven expectations and in close collaboration with agronomists and computer engineers. It integrates an innovative algorithm developed for projections of machinery costs under different rates of annual machine use and work capacity processing, which is crucial to decisions on break-even machinery alternatives. A case study based on the comparison of multiple alternatives for grape harvesting operations is presented to demonstrate the typical results that can be expected from MACHoice, and to identify its capabilities and limitations. This DSS offers an integrated and flexible analysis environment with a user-friendly graphical interface as well as a high level of automation of processing chains. The DSS-output consists of charts and tables, evidencing the differences related to costs and carbon emissions between the options inserted by the user for the different intensity of yearly work proceeded. MACHoice is an interactive web-based tool that can be accessed freely for non-commercial use by every known browser.
M. Cunha; S.G. Gonçalves. MACHoice: a Decision Support System for agricultural machinery management. Open Agriculture 2019, 4, 305 -321.
AMA StyleM. Cunha, S.G. Gonçalves. MACHoice: a Decision Support System for agricultural machinery management. Open Agriculture. 2019; 4 (1):305-321.
Chicago/Turabian StyleM. Cunha; S.G. Gonçalves. 2019. "MACHoice: a Decision Support System for agricultural machinery management." Open Agriculture 4, no. 1: 305-321.
This study aimed to estimate the daily crop evapotranspiration (ETc) of soilless cut ‘Red Naomi’ roses, cultivated in a commercial glass greenhouse, using climatic and crop predictors. A multiple stepwise regression technique was applied for estimating ETc using the daily relative humidity, stem leaf area and number of leaves of the bended stems. The model explained 90% of the daily ETc variability (R2 = 0.90, n = 33, P < 0.0001) measured by weighing lysimeters. The mean relative difference between the observed and the estimated daily ETc was 9.1%. The methodology revealed a high accuracy and precision in the estimation of daily ETc.
Ana Costa; Isabel Pôças; Mario Cunha. Modelling evapotranspiration of soilless cut roses ‘Red Naomi’ based on climatic and crop predictors. Horticultural Science 2019, 46, 107 -114.
AMA StyleAna Costa, Isabel Pôças, Mario Cunha. Modelling evapotranspiration of soilless cut roses ‘Red Naomi’ based on climatic and crop predictors. Horticultural Science. 2019; 46 (No. 2):107-114.
Chicago/Turabian StyleAna Costa; Isabel Pôças; Mario Cunha. 2019. "Modelling evapotranspiration of soilless cut roses ‘Red Naomi’ based on climatic and crop predictors." Horticultural Science 46, no. No. 2: 107-114.
An Automatic Calibration of Fertilizers (ACFert) system was developed, for use with centrifugal, pendulum or other types of broadcast spreaders which distribute dry granular agricultural materials on the top of the soil. The ACfert is based on image processing techniques and includes a specially designed mat, which should be placed in the ground for spreaders calibration. A set of images acquired outdoor by a standard device (simple camera) is used to extract information about the spreader distribution pattern. Each image is processed independently, providing as output two numerical values for each grid element present in the image – the number of fertilizers/seeds counted, and its numerical label. The performance of ACFert was evaluated for automatic granules detection using a set of manual counting measurements of nitrate fertilizer and wheat seeds. A total of 185 images acquired with two mobiles devices were used with a total of 498 quadrilateral elements observed and analysed. The overall mean absolute relative error between counting and computed by the ACFert system, were 0.75 ± 0.75% for fertilizer and 2.12 ± 1.68% for wheat. This near real-time calibration tool is a very low cost system that can be easily used on field, providing results to support accurate spreader calibration in near real time for different types of fertilizers or seeds.
André R.S. Marcal; Mario Cunha. Development of an image-based system to assess agricultural fertilizer spreader pattern. Computers and Electronics in Agriculture 2019, 162, 380 -388.
AMA StyleAndré R.S. Marcal, Mario Cunha. Development of an image-based system to assess agricultural fertilizer spreader pattern. Computers and Electronics in Agriculture. 2019; 162 ():380-388.
Chicago/Turabian StyleAndré R.S. Marcal; Mario Cunha. 2019. "Development of an image-based system to assess agricultural fertilizer spreader pattern." Computers and Electronics in Agriculture 162, no. : 380-388.
Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. For this end, we compared the performance of different remotely-sensed variables to discriminate burned areas, by applying a simple change-point analysis procedure on time-series of MODIS imagery for the northern half of Portugal, without external information (e.g. active fire maps). Overall, our results highlight the importance of adopting a multi-indicator consensus approach for mapping and detecting wildfire disturbances at a regional scale, that allows to profit from spectral indices capturing different aspects of the Earth's surface, and derived from distinct regions of the electromagnetic spectrum. Finally, we argue that the framework here described can be used: (i) in a wide variety of geographical and environmental contexts; (ii) to support the identification of the best possible remotely-sensed functional indicators of wildfire disturbance; and (iii) for improving and complementing incomplete wildfire databases.
Bruno Marcos; João Gonçalves; Domingo Alcaraz-Segura; Mario Cunha; Joao Honrado. Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation 2019, 78, 77 -85.
AMA StyleBruno Marcos, João Gonçalves, Domingo Alcaraz-Segura, Mario Cunha, Joao Honrado. Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal. International Journal of Applied Earth Observation and Geoinformation. 2019; 78 ():77-85.
Chicago/Turabian StyleBruno Marcos; João Gonçalves; Domingo Alcaraz-Segura; Mario Cunha; Joao Honrado. 2019. "Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal." International Journal of Applied Earth Observation and Geoinformation 78, no. : 77-85.
Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.
Sosdito Mananze; Isabel Pôças; Mario Cunha. Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data. Remote Sensing 2018, 10, 1942 .
AMA StyleSosdito Mananze, Isabel Pôças, Mario Cunha. Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data. Remote Sensing. 2018; 10 (12):1942.
Chicago/Turabian StyleSosdito Mananze; Isabel Pôças; Mario Cunha. 2018. "Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data." Remote Sensing 10, no. 12: 1942.