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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images
Simone Pietro Garofalo, Vincenzo Giannico, Beatriz Lorente, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 10

Showing 10 citing articles:

Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence
Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, et al.
Agronomy (2025) Vol. 15, Iss. 1, pp. 241-241
Open Access | Times Cited: 1
A Machine Learning Approach to Monitor the Physiological and Water Status of an Irrigated Peach Orchard under Semi-Arid Conditions by Using Multispectral Satellite Data
Pasquale Campi, Anna Francesca Modugno, Gabriele De Carolis, et al.
Water (2024) Vol. 16, Iss. 16, pp. 2224-2224
Open Access | Times Cited: 7
Ecophysiological Recovery of Micropropagated Olive Cultivars: Field Research in an Irrigated Super-High-Density Orchard
Francesco Maldera, Simone Pietro Garofalo, Salvatore Camposeo
Agronomy (2024) Vol. 14, Iss. 7, pp. 1560-1560
Open Access | Times Cited: 5
Ecological optima show the potential diffusion of minor tree crops in Xylella fastidiosa subsp. pauca-infected areas through a GIS-based approach
Leonardo Costanza, Francesco Maldera, Simone Pietro Garofalo, et al.
Frontiers in Agronomy (2024) Vol. 6
Open Access | Times Cited: 4
Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment
Leonardo Costanza, Beatriz Lorente, Francisco Pedrero Salcedo, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2746-2746
Open Access
Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation
Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, et al.
Agricultural Water Management (2024) Vol. 306, pp. 109163-109163
Open Access | Times Cited: 2
Explainable Artificial Intelligence to Predict the Water Status of Cotton (Gossypium hirsutum L., 1763) from Sentinel-2 Images in the Mediterranean Area
Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, et al.
Plants (2024) Vol. 13, Iss. 23, pp. 3325-3325
Open Access | Times Cited: 2
Architectural approach to evaluate the design and management of almond cultivars suitable for super high-density orchards
Francesco Maldera, Simone Pietro Garofalo, Salvatore Camposeo
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 1
Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions
Vincenzo Giannico, Simone Pietro Garofalo, Luca Brillante, et al.
Remote Sensing (2024) Vol. 16, Iss. 24, pp. 4784-4784
Open Access | Times Cited: 1
Visual Servoing for Aerial Vegetation Sampling Systems
Zahra Samadikhoshkho, Michael Lipsett
Drones (2024) Vol. 8, Iss. 11, pp. 605-605
Open Access
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