Mário Cunha

3.8k total citations
139 papers, 2.9k citations indexed

About

Mário Cunha is a scholar working on Plant Science, Ecology and Global and Planetary Change. According to data from OpenAlex, Mário Cunha has authored 139 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Plant Science, 54 papers in Ecology and 31 papers in Global and Planetary Change. Recurrent topics in Mário Cunha's work include Remote Sensing in Agriculture (45 papers), Horticultural and Viticultural Research (38 papers) and Smart Agriculture and AI (19 papers). Mário Cunha is often cited by papers focused on Remote Sensing in Agriculture (45 papers), Horticultural and Viticultural Research (38 papers) and Smart Agriculture and AI (19 papers). Mário Cunha collaborates with scholars based in Portugal, Spain and Brazil. Mário Cunha's co-authors include Isabel Pôças, Ilda Abreu, Helena Ribeiro, L. S. Pereira, A. Marçal, Filipe Neves dos Santos, Sandro Augusto Magalhães, Ana Cláudia Teodoro, Isidro Campos and Alfonso Calera and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and Scientific Reports.

In The Last Decade

Mário Cunha

135 papers receiving 2.8k citations

Peers

Countries citing papers authored by Mário Cunha

Since Specialization

Citations

This map shows the geographic impact of Mário Cunha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mário Cunha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mário Cunha more than expected).

Fields of papers citing papers by Mário Cunha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mário Cunha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mário Cunha. The network helps show where Mário Cunha may publish in the future.

Co-authorship network of co-authors of Mário Cunha

This figure shows the co-authorship network connecting the top 25 collaborators of Mário Cunha. A scholar is included among the top collaborators of Mário Cunha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mário Cunha. Mário Cunha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification 2024 ·Computers and Electronics in Agriculture ·Filipe Monteiro-Silva, Carla Queirós, (unknown), Tatiana M. Pinho, (unknown), S. Magalhães, José Boaventura‐Cunha, Filipe Neves dos Santos, Mário Cunha, Rui C. Martins	17
2	Grapevine inflorescence segmentation and flower estimation based on Computer Vision techniques for early yield assessment 2024 ·SHILAP Revista de lepidopterología ·(unknown), Filipe Neves dos Santos, Mário Cunha	1
3	Deep learning based approach for actinidia flower detection and gender assessment 2024 ·Scientific Reports ·(unknown), (unknown), Sandro Augusto Magalhães, António Valente, Mário Cunha, Filipe Neves dos Santos	1
4	Plant Disease Diagnosis Based on Hyperspectral Sensing: Comparative Analysis of Parametric Spectral Vegetation Indices and Nonparametric Gaussian Process Classification Approaches 2024 ·Agronomy ·(unknown), Jochem Verrelst, (unknown), Juan Pablo Rivera, Fernando Tavares, Filipe Neves dos Santos, Mário Cunha	7
5	Early plant disease diagnosis through handheld UV–Vis transmittance spectrometer with DD-SIMCA one-class classification and MCR-ALS bilinear decomposition 2024 ·SHILAP Revista de lepidopterología ·(unknown), Sarmento J. Mazivila, Fernando Tavares, Filipe Neves dos Santos, Mário Cunha	3
6	Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions 2023 ·Agronomy ·(unknown), (unknown), (unknown), Sandro Augusto Magalhães, António Valente, Paulo Moura Oliveira, Mário Cunha, Filipe Neves dos Santos	23
7	LIBS-Based Analysis of Elemental Composition in Skin, Pulp, and Seeds of White and Red Grape Cultivars 2023 ·SHILAP Revista de lepidopterología ·(unknown), Filipe Monteiro-Silva, Rui C. Martins, Mário Cunha	1
8	In-Field Hyperspectral Proximal Sensing for Estimating Grapevine Water Status to Support Smart Precision Viticulture 2023 ·SHILAP Revista de lepidopterología ·(unknown), (unknown), (unknown), (unknown), Catarina Barbosa, Filipe Neves dos Santos, Hugo Alves Pinheiro, Rui C. Martins, Mário Cunha	0
9	Enhancing Kiwi Bacterial Canker Leaf Assessment: Integrating Hyperspectral-Based Vegetation Indexes in Predictive Modeling 2023 ·SHILAP Revista de lepidopterología ·(unknown), (unknown), Rui C. Martins, Filipe Neves dos Santos, Fernando Tavares, Mário Cunha	4
10	Canopy VIS-NIR spectroscopy and self-learning artificial intelligence for a generalised model of predawn leaf water potential in Vitis vinifera 2022 ·Biosystems Engineering ·(unknown), Rui C. Martins, Isabel Pôças, Mário Cunha	20
11	Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images 2022 ·Remote Sensing ·Yahui Guo, Shouzhi Chen, Xinxi Li, Mário Cunha, J. Senthilnath, Davide Cammarano, Yongshuo H. Fu	115
12	The Phenolic Composition of Hops (Humulus lupulus L.) Was Highly Influenced by Cultivar and Year and Little by Soil Liming or Foliar Spray Rich in Nutrients or Algae 2022 ·Horticulturae ·(unknown), María Inês Días, Isabel C.F.R. Ferreira, Margarida Arrobas, Mário Cunha, Lillian Barros, Manuel Ângelo Rodrigues	8
13	Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse 2021 ·Sensors ·Sandro Augusto Magalhães, (unknown), (unknown), Filipe Neves dos Santos, Mário Cunha, Jorge Dias, António Paulo Moreira	120
14	Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems 2021 ·Diversity ·António T. Monteiro, Paulo Alves, Cláudia Carvalho‐Santos, Richard Lucas, Mário Cunha, Eduarda Marques da Costa, Francesco Fava	4
15	Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal 2021 ·Land ·Lia Duarte, Mário Cunha, Ana Cláudia Teodoro	13
16	Assessing predawn leaf water potential based on hyperspectral data and pigment’s concentration of Vitis vinifera L. in the Douro Wine Region 2020 ·Scientia Horticulturae ·(unknown), Isabel Pôças, (unknown), Jorge Teixeira, Natacha Fontes, António Graça, Mário Cunha	17
17	Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique 2020 ·Remote Sensing ·(unknown), Isabel Pôças, Mário Cunha	32
18	Comparison of harvest-related removal of aboveground biomass, carbon and nutrients in pedunculate oak stands and in fast-growing tree stands in NW Spain 2016 ·Forest Ecology and Management ·Esteban Gómez-García, Ulises Diéguez‐Aranda, Mário Cunha, Roque Rodríguez‐Soalleiro	23
19	Pollen-based predictive modelling of wine production: application to an arid region 2015 ·European Journal of Agronomy ·Mário Cunha, Helena Ribeiro, Ilda Abreu	34
20	Influence of meteorological parameters on Olea flowering date and airborne pollen concentration in four regions of Portugal 2006 ·Grana ·Helena Ribeiro, (unknown), Ilda Abreu, Mário Cunha	20

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact