Agriculture
This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
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This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
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3 Views
This is a wheat breeding phenotyping and yield dataset, including canopy height (CH, m), canopy volume (CV, m3), and leaf area index (LAI) collected in the field; vegetation index (VI) generated by multispectral data acquired by UAV remote sensing; trial site weather (Weather); and yield (Yield, kg). The data comes from field trials.
Data acquisition and processing are described in the relevant part of the manuscript.
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131 Views
Hyperspectral imaging (HSI) has become a pivotal tool for environmental monitoring, particularly in identifying and analyzing hydrocarbon spills. This study presents an Internet of Things (IoT)-based framework for the collection, management, and analysis of hyperspectral data, employing a controlled experimental setup to simulate hydrocarbon contamination. Using a state-of-the-art hyperspectral camera, a dataset of 116 images was generated, encompassing temporal and spectral variations of gasoline, thinner, and motor oil spills.
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144 Views
The data is collected from the deployed IoT sensor node at a pilot farm in Narrabri, Australia. The dataset includes information about soil characteristics such as soil moisture and soil temperature at 20-40-60 cm depth. The sensor node also provides information about environmental influencers, which are critical in constructing machine learning models to predict Evapotranspiration in diverse soil and environmental conditions.
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432 Views
This dataset provides comprehensive data for predicting the most suitable fertilizer for various crops based on environmental and soil conditions. It includes environmental factors like temperature, humidity, and moisture, along with soil and crop types, and nutrient composition (Nitrogen, Potassium, and Phosphorous). The target variable is the recommended fertilizer name.
The data is already pre-processed without anu Null values.
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386 Views
Este proyecto se desarrolló para optimizar el análisis de suelos agrícolas en cultivos de papa, con un enfoque en mejorar la precisión y accesibilidad de los diagnósticos de nutrientes esenciales (NPK) a través de tecnología de sensores. En primer lugar, se realizó la calibración de sensores industriales multiparámetro (CWT para NPK y CWT-multiparámetro), basándose en valores de referencia de laboratorios convencionales, lo que permitió configurar un marco de medición confiable.
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234 Views
The dataset contains the ground-based observations of crop growth stages for Canada's prairie provinces (Manitoba, Saskatchewan and Alberta) from 2019 to 2020. Crop growth stages were visually observed from the side of the fields on a weekly cycle until the fields were harvested. The BBCH (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie) scale was used to stage growth.
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494 Views
The dataset provides crop-type surveys for Canada's prairie provinces (Manitoba, Saskatchewan and Alberta) in 2020 and 2021. The data were collected via windshield survey(driving through the countryside with GPS-enabled data collection software and satellite imagery). Crop-type points and their geographic coordinates on the ground were gathered using data collection software. Field boundaries were identified on satellite imagery. A single observation point is dropped in a homogeneous area within the field.
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In this research, a newly modified UNet (Fast-UNet) was implemented to segment winter wheat from time series Sentinel-2 images for the years 2021 and 2023. These images were converted to NDVI and utilized to identify wheat fields by tracking the wheat phenology from sowing to harvesting. The main satellite image that was used in this research was Sentinel-2. It is considered important, and free optical remote sensing satellite data is provided by the European Space Agency (ESA). Sentinel-2A and Sentinel-2B were launched in June 2015 and March 2017, respectively.
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240 Views