Agriculture
Bananas are widely farmed and consumed, offering essential nutrients like manganese, vitamin B6, vitamin C, and magnesium. They come in various breeds with distinct visual traits, including size, shape, color, texture, and skin patterns. To classify these varieties, five deep learning models—VGG16, ResNet50, MobileNet, Inception-v3, and a customized CNN—were trained on banana images. These models enhance quality control and supply chain management by accurately identifying banana breeds.
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Solar Insecticidal Lamps Internet of Things (SIL-IoTs) is an advanced agricultural IoT system integrating solar insecticidal lamps with wireless sensor networks. It attracts pests with light, then kills them with high-voltage metal grids. Equipped with wireless communication modules and environmental sensors, SIL-IoTs can collect and transmit field data, including pest counts (discharge pulse counts, insect-killing sound pulse counts), environmental data (air temperature, humidity, light intensity, equipment box temperature), and operational status.
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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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