Agriculture
This study introduces a high-resolution UAV (Unmanned Aerial Vehicle) remote sensing image dataset aimed at advancing the development of deep learning-based farmland boundary extraction techniques and supporting the optimal deployment of Solar Insect Lights (SILs). Agricultural pests pose a significant threat to crop health and yield, while traditional pest control methods often cause environmental pollution.
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Solar-powered insecticidal lamps have been widely used in agricultural pest control systems, where stable 4G connectivity is critical for real-time transmission of multi-source field data (soil parameters, pest images, and environmental metrics). However, the lack of reliable 4G signal strength datasets in agricultural scenarios, especially under rainfall conditions that cause signal degradation, poses a great challenge to deployment planning and network reliability.
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Agriculture is the backbone of Mizoram’s state economy as the majority of the people use agriculture and its allied sector as their livelihood. According to the 2011 census, more than 50% of the people are still engaged in agriculture and its related activities. Jhum cultivation or shifting cultivation is the primary farming pattern in the state.
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The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed.
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With the gradual maturity of UAV technology, it can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Therefore, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote sensing data from 8 walnut sample plots.
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The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6 viral diseases, and 3 mite-induced diseases, categorized into 97 classes.
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This is a dataset containing images of cotton leaves with Verticillium wilt, brown spot, aphids, and healthy leaves.The dataset initially consisted of original images of brown spot disease (330 images), verticillium wilt (213 images), healthy leaves (383 images), and aphids (473 images). To balance class distributions and improve model performance, data augmentation techniques such as flipping and scaling were applied.
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This dataset includes spectra of 250 corn samples with different vitality levels, with a data size of 250*256, categorized into five vitality grades. The imaging spectrometer employs a series spectrophotometer, model N17E, with a spectral range of 874-1734nm and a spectral resolution of 5nm. The CCD used is model ICL-B1410, featuring 1600×1200 pixels, and is equipped with an OLES22 lens with a focal length of 22mm.
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Ripe and unripe pistachios lies in their appearance, taste, and texture, as well as their uses. Ripe Pistachio: The kernel inside is vibrant green with purple skin, larger, and fully developed. More aromatic and flavorful than unripe pistachios. Widely used in desserts (ice creams, baklava, pastries) and savory dishes. Unripe Pistachio: The nut kernel is smaller and pale green or yellowish. Less sweet and not as flavorful as ripe pistachios. Sometimes used in specialty cuisines, pickling, or as a garnish. This dataset contains 966 images for ripe and 966 images for unripe classes.
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