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Simone Pietro Garofalo

University of Bari Aldo Moro

H指数:10

被引数:231

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An interpretable machine learning framework to predict Relative Water Content by using hyperspectral data in Phaseolus vulgaris L. under different irrigation conditions

基于高光谱数据预测不同灌溉条件下菜豆相对含水量的可解释机器学习框架

Smart Agricultural Technology

IF5.7

2026-07-140

OAAI

Flowering Phenograms and Genetic Sterilities of Ten Olive Cultivars Grown in a Super-High-Density Orchard

IF3

2026-01-250

OAAI

Use of explainable artificial intelligence with high-resolution satellite imagery to assess water status and vine shoot growth in grapevine (Vitis vinifera L.)

利用可解释人工智能与高分辨率卫星影像评估葡萄藤（Vitis vinifera L.）的水分状况和枝条生长

Modeling Earth Systems and Environment

IF2.9

2025-11-070

OAAI

Automated On-Tree Detection and Size Estimation of Pomegranates by a Farmer Robot

基于农民机器人的石榴自动树上检测与大小估计

Robotics 2025, Vol. 14, Page 131

IF3.3

2025-09-300

OAAI

Assessing Soil Water Content of Regenerative Cotton Crop with Extreme Gradient Boosting from Agrometeorological and Satellite Data

基于极端梯度提升方法，利用农业气象和卫星数据评估再生棉作物的土壤含水量

APPLIED SCIENCES-BASEL

IF2.5

2025-09-070

OAAI

Energy of Sorghum Biomass Under Deficit Irrigation Strategies in the Mediterranean Area

IF3

2025-02-170

OA

Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. & Nakai, 1916)

IF3

2025-01-230

OA

Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence

IF3.4

2025-01-191

OA

Explainable Artificial Intelligence to Predict the Water Status of Cotton (Gossypium hirsutum L., 1763) from Sentinel-2 Images in the Mediterranean Area

IF4.1

2024-11-273

OAAI

Physical Ripening Indices Improve the Assessment of Mechanical Harvesting Time for Olive Cultivars Resistant to Xylella fastidiosa subsp. pauca

IF3

2024-10-180

OA

Ecological optima show the potential diffusion of minor tree crops in Xylella fastidiosa subsp. pauca-infected areas through a GIS-based approach

FRONTIERS IN AGRONOMY

IF4.1

2024-08-225

OA

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

IF3

2024-08-069

OA

Architectural approach to evaluate the design and management of almond cultivars suitable for super high-density orchards

FRONTIERS IN PLANT SCIENCE

IF4.8

2024-07-230

OAAI

Ecophysiological Recovery of Micropropagated Olive Cultivars: Field Research in an Irrigated Super-High-Density Orchard

IF3.4

2024-07-186

OA

Qualitative and biochemical characteristics of pomegranate fruit grown using reclaimed water and low input fertigation treatments at harvest and during storage

IF3.6

2024-07-010

OAAI

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images

FRONTIERS IN PLANT SCIENCE

IF4.8

2024-03-1911

OAAI

Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques

IF3.4

2023-12-1916

OA

Agronomic Responses of Grapevines to an Irrigation Scheduling Approach Based on Continuous Monitoring of Soil Water Content

IF3.4

2023-11-159

OA

Land Suitability Analysis of Six Fruit Tree Species Immune/Resistant to Xylella fastidiosa as Alternative Crops in Infected Olive-Growing Areas

IF3.4

2023-02-1414

OA

In-Field Automatic Identification of Pomegranates Using a Farmer Robot

IF3.5

2022-08-0411

OAAI