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  • Rural Digital Europe
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Muhammad Faheem; Mahmoud Ahmad Al-Khasawneh; Arfat Ahmad Khan; Syed Hamid Hussain Madni;

    Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.

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    Data in Brief
    Article . 2024 . Peer-reviewed
    License: CC BY
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Data in Brief
    Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Data in Briefarrow_drop_down
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      Data in Brief
      Article . 2024 . Peer-reviewed
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      Data in Brief
      Article . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mohammad Imtiaz Nakib; M.F. Mridha;

    Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.

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    Data in Brief
    Article . 2024 . Peer-reviewed
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Data in Brief
    Article . 2024
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      Data in Brief
      Article . 2024 . Peer-reviewed
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      Data in Brief
      Article . 2024
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    Authors: Kingsley Umani; Chongyuan Zhang; Rebecca J. McGee; George J. Vandemark; +1 Authors

    Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the “Palouse” region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20–78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.

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    Data in Brief
    Article . 2024 . Peer-reviewed
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    Data in Brief
    Article . 2024
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      Data in Brief
      Article . 2024 . Peer-reviewed
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      Data in Brief
      Article . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marco Esposito; Simone Marzorati; Alberto Belli; Chiara Ladina; +4 Authors

    This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources.The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Data in Briefarrow_drop_down
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    Data in Brief
    Article . 2024 . Peer-reviewed
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    Data in Brief
    Article . 2024
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      Article . 2024 . Peer-reviewed
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      Article . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Md Ripon Sheikh; Md. Masudul Islam; Galib Muhammad Shahriar Himel;

    This article introduces a comprehensive dataset designed for researchers to classify diseases in Luffa leaves, determine the grade of Luffa from Luffa images, and identify different growth stages throughout the year. The dataset is meticulously organized into three sections, each concentrating on specific facets of Luffa Aegyptiaca, commonly known as Smooth Luffa (Dhundol/). These images were captured in various village fields in Faridpur, Bangladesh. The sections include the assessment of Smooth Luffa quality, the identification of plant diseases, and the documentation of Luffa flowers. The dataset is divided into three sections, totaling 1933 original JPG images. The “Luffa Diseases” section features images of smooth Luffa leaves, depicting various diseases and unaffected leaves. Categories in this section encompass Alternaria Disease, Angular Spot Disease, Holed Leaves, Mosaic Virus, and Fresh Leaves, totaling 1228 JPG raw images. The “Flowers” category comprises 362 JPG raw images, showcasing different maturity stages in smooth Luffa flowers. Finally, the “Luffa Grade” section focuses on categorizing smooth Luffa into fresh and defective categories, presenting 343 JPG raw images for this purpose.

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    Article . 2024 . Peer-reviewed
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    Authors: Michael J. Justina; M. Thenmozhi;

    The intuition behind this data acquisition is to encourage research for addressing the problem of weeds in agriculture through computer vision applications. Data is acquired in the form of images from uniform and random crop-spacing fields. In other words, we have taken a step forward to identify weeds from fields that follow any method of sowing, which ultimately leads to the transformation of traditional agriculture into precision agriculture. Sorghum crop and its associated weeds are chosen as the research objects during this process. These acquired data are used in framing two datasets. The first dataset termed ‘SorghumWeedDataset_Classification’ is a crop-weed classification dataset created with 4312 data samples for addressing crop-weed classification problems. The second dataset termed ‘SorghumWeedDataset_Segmentation’ is a crop-weed segmentation dataset that contains 5555 manually pixel-wise annotated data segments from 252 data samples for addressing crop-weed localization, detection, and segmentation problems. All data samples are acquired in April and May 2023 from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. Manually annotated data samples and data segments are verified by agronomists. The datasets are made publicly available to the research community to solve the crop-weed problems using state-of-the-art image processing, machine learning, and deep learning algorithms. To the best of our knowledge, these are the first open-access crop-weed research datasets from Indian fields for classification and segmentation to deal with weed issues in uniform and random crop-spacing fields. However, other available datasets (from Indian fields) are either non-research datasets or available on subscription/request.

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    Article . 2024
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    Data in Brief
    Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Nabila Husna Shabrina; Siwi Indarti; Rina Maharani; Dinar Ajeng Kristiyanti; +3 Authors

    Potatoes are of the utmost importance for both food processing and daily consumption; however, they are also prone to pests and diseases, which can cause significant economic losses. To address this issue, the implementation of image processing and computer vision methods in conjunction with machine learning and deep learning techniques can serve as an alternative approach for quickly identifying diseases in potato leaves. Several studies have demonstrated promising results. However, the current research is limited by the use of a single dataset, the PlantVillage dataset, which may not accurately represent the diverse conditions of potato pests and diseases in real-world settings. Therefore, a new dataset that accurately depicts various types of diseases is crucial. We propose a novel dataset that offers several advantages over previous datasets, including data obtained in an uncontrolled environment that results in a diverse range of variables such as background and image angles. The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease identification.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tania Khatun; Md. Asraful Sharker Nirob; Prayma Bishshash; Morium Akter; +1 Authors

    Dragon fruit, often referred to as pitaya, is a tropical fruit with various types, including both white-fleshed and red-fleshed varieties. Its distinctive appearance is complemented by a range of potential health advantages. These include its abundance of nutrients and antioxidants, which contribute to a robust immune system, aid in blood sugar regulation, and support the well-being of the heart, bones, and skin. Consequently, the global desire for dragon fruit is yielding substantial economic advantages for developing nations like Bangladesh, which in turn underscores the pressing need for an automated system to identify the optimal harvest time and differentiate between fresh and defective fruits to ensure quality. To accomplish this objective, this paper introduces an extensive collection of high-resolution dragon fruits because effective detection by machine learning models necessitates a substantial amount of data. The dataset was painstakingly gathered during a span of four months from three distinct locations in Bangladesh, with the valuable assistance of domain experts. Possible application of the dataset encompasses quality evaluation, robotic harvesting, and packaging systems, ultimately boosting the effectiveness of dragon fruit production procedures. The dataset has the potential to be a valuable resource for researchers interested in dragon fruit cultivation, offering a solid foundation for the application of computer vision and deep learning methods in the agricultural industry.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hamdi A. Zurqani;

    Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R2 values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world.

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    Authors: Masuly Vega-Puga; Jaime Garatuza-Payán; Miguel A. Rivera-Diaz; Onésimo Galaz; +2 Authors

    Unmanned Aerial Vehicles (UAV) are advantageous to assess vegetation and terrain changes at a high spatial resolution, advancing our understanding of shifts in ecosystem states and processes. The extension of land that we sought to observe belongs to the footprint of an Eddy Covariance System (around 7.5 ha), installed in a secondary ecological succession of the tropical dry forest (27.00598005 N, 108.77913821 W) within the Sierra de Álamos-Rio Cuchujaqui natural protected area, Sonora, Mexico. Aerial images were obtained with a system of multispectral and RGB cameras mounted on a UAV, which executed automated missions in two different seasons: 1) dry season (June 2023) and 2) first rain season (July 2023), totalling 1116 images. The UAV mission was flown at a height of 105 m and images included a side and front overlap of 70%. The final product was an image dataset per season. These data can contribute to the continuous monitoring strategy to understand ecosystem processes and conserve the Sierra de Álamos-Rio Cuchujaqui natural protected area, a critical natural protected area for national and international organizations. Likewise, image datasets are useful for the processes of developing regional models of phenology, structure, richness, distribution, and vegetation cover along secondary succession after the forest was changed to agriculture and livestock land.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Muhammad Faheem; Mahmoud Ahmad Al-Khasawneh; Arfat Ahmad Khan; Syed Hamid Hussain Madni;

    Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.

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    Authors: Mohammad Imtiaz Nakib; M.F. Mridha;

    Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.

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    Authors: Kingsley Umani; Chongyuan Zhang; Rebecca J. McGee; George J. Vandemark; +1 Authors

    Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the “Palouse” region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20–78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.

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    Authors: Marco Esposito; Simone Marzorati; Alberto Belli; Chiara Ladina; +4 Authors

    This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources.The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

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    Authors: Md Ripon Sheikh; Md. Masudul Islam; Galib Muhammad Shahriar Himel;

    This article introduces a comprehensive dataset designed for researchers to classify diseases in Luffa leaves, determine the grade of Luffa from Luffa images, and identify different growth stages throughout the year. The dataset is meticulously organized into three sections, each concentrating on specific facets of Luffa Aegyptiaca, commonly known as Smooth Luffa (Dhundol/). These images were captured in various village fields in Faridpur, Bangladesh. The sections include the assessment of Smooth Luffa quality, the identification of plant diseases, and the documentation of Luffa flowers. The dataset is divided into three sections, totaling 1933 original JPG images. The “Luffa Diseases” section features images of smooth Luffa leaves, depicting various diseases and unaffected leaves. Categories in this section encompass Alternaria Disease, Angular Spot Disease, Holed Leaves, Mosaic Virus, and Fresh Leaves, totaling 1228 JPG raw images. The “Flowers” category comprises 362 JPG raw images, showcasing different maturity stages in smooth Luffa flowers. Finally, the “Luffa Grade” section focuses on categorizing smooth Luffa into fresh and defective categories, presenting 343 JPG raw images for this purpose.

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    Authors: Michael J. Justina; M. Thenmozhi;

    The intuition behind this data acquisition is to encourage research for addressing the problem of weeds in agriculture through computer vision applications. Data is acquired in the form of images from uniform and random crop-spacing fields. In other words, we have taken a step forward to identify weeds from fields that follow any method of sowing, which ultimately leads to the transformation of traditional agriculture into precision agriculture. Sorghum crop and its associated weeds are chosen as the research objects during this process. These acquired data are used in framing two datasets. The first dataset termed ‘SorghumWeedDataset_Classification’ is a crop-weed classification dataset created with 4312 data samples for addressing crop-weed classification problems. The second dataset termed ‘SorghumWeedDataset_Segmentation’ is a crop-weed segmentation dataset that contains 5555 manually pixel-wise annotated data segments from 252 data samples for addressing crop-weed localization, detection, and segmentation problems. All data samples are acquired in April and May 2023 from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. Manually annotated data samples and data segments are verified by agronomists. The datasets are made publicly available to the research community to solve the crop-weed problems using state-of-the-art image processing, machine learning, and deep learning algorithms. To the best of our knowledge, these are the first open-access crop-weed research datasets from Indian fields for classification and segmentation to deal with weed issues in uniform and random crop-spacing fields. However, other available datasets (from Indian fields) are either non-research datasets or available on subscription/request.

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    Authors: Nabila Husna Shabrina; Siwi Indarti; Rina Maharani; Dinar Ajeng Kristiyanti; +3 Authors

    Potatoes are of the utmost importance for both food processing and daily consumption; however, they are also prone to pests and diseases, which can cause significant economic losses. To address this issue, the implementation of image processing and computer vision methods in conjunction with machine learning and deep learning techniques can serve as an alternative approach for quickly identifying diseases in potato leaves. Several studies have demonstrated promising results. However, the current research is limited by the use of a single dataset, the PlantVillage dataset, which may not accurately represent the diverse conditions of potato pests and diseases in real-world settings. Therefore, a new dataset that accurately depicts various types of diseases is crucial. We propose a novel dataset that offers several advantages over previous datasets, including data obtained in an uncontrolled environment that results in a diverse range of variables such as background and image angles. The proposed dataset comprises 3076 images categorized into seven classes, including leaves attacked by viruses, bacteria, fungi, pests, nematodes, phytophthora, and healthy leaves. This dataset aims to provide a more accurate representation of potato leaf diseases and facilitate advancements in the current research on potato leaf disease identification.

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    Authors: Tania Khatun; Md. Asraful Sharker Nirob; Prayma Bishshash; Morium Akter; +1 Authors

    Dragon fruit, often referred to as pitaya, is a tropical fruit with various types, including both white-fleshed and red-fleshed varieties. Its distinctive appearance is complemented by a range of potential health advantages. These include its abundance of nutrients and antioxidants, which contribute to a robust immune system, aid in blood sugar regulation, and support the well-being of the heart, bones, and skin. Consequently, the global desire for dragon fruit is yielding substantial economic advantages for developing nations like Bangladesh, which in turn underscores the pressing need for an automated system to identify the optimal harvest time and differentiate between fresh and defective fruits to ensure quality. To accomplish this objective, this paper introduces an extensive collection of high-resolution dragon fruits because effective detection by machine learning models necessitates a substantial amount of data. The dataset was painstakingly gathered during a span of four months from three distinct locations in Bangladesh, with the valuable assistance of domain experts. Possible application of the dataset encompasses quality evaluation, robotic harvesting, and packaging systems, ultimately boosting the effectiveness of dragon fruit production procedures. The dataset has the potential to be a valuable resource for researchers interested in dragon fruit cultivation, offering a solid foundation for the application of computer vision and deep learning methods in the agricultural industry.

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      Data in Brief
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    Authors: Hamdi A. Zurqani;

    Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R2 values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world.

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    Data in Brief
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    Data in Brief
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    Authors: Masuly Vega-Puga; Jaime Garatuza-Payán; Miguel A. Rivera-Diaz; Onésimo Galaz; +2 Authors

    Unmanned Aerial Vehicles (UAV) are advantageous to assess vegetation and terrain changes at a high spatial resolution, advancing our understanding of shifts in ecosystem states and processes. The extension of land that we sought to observe belongs to the footprint of an Eddy Covariance System (around 7.5 ha), installed in a secondary ecological succession of the tropical dry forest (27.00598005 N, 108.77913821 W) within the Sierra de Álamos-Rio Cuchujaqui natural protected area, Sonora, Mexico. Aerial images were obtained with a system of multispectral and RGB cameras mounted on a UAV, which executed automated missions in two different seasons: 1) dry season (June 2023) and 2) first rain season (July 2023), totalling 1116 images. The UAV mission was flown at a height of 105 m and images included a side and front overlap of 70%. The final product was an image dataset per season. These data can contribute to the continuous monitoring strategy to understand ecosystem processes and conserve the Sierra de Álamos-Rio Cuchujaqui natural protected area, a critical natural protected area for national and international organizations. Likewise, image datasets are useful for the processes of developing regional models of phenology, structure, richness, distribution, and vegetation cover along secondary succession after the forest was changed to agriculture and livestock land.

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    Data in Brief
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