Agriculture

The Lemon Leaf Disease Dataset (LLDD) is a high-quality image dataset designed for training and evaluating machine learning models for lemon leaf disease classification. The dataset contains 9  classes of images of healthy and diseased lemon leaves, such as; Anthracnose. Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites, making it suitable for tasks such as plant disease instance segmentation, detection, image classification, and deep learning applications in agriculture.

we develop a spatio-spectral-temporal deep learning regression model , termed CASST-Net, which leverages a cosine attention mechanism to enhance feature representation to improve FVC estimation accuracy, can also dynamically adjusted the weighting values of each spectral band in the satellite data based on changes in vegetation physiological characteristics and canopy structure.

This dataset comprises 30 CSV files featuring text-based narratives developed as part of the MOVING (MOuntain Valorisation through INterconnectedness and Green Growth) Horizon 2020 project, which explores 454 value chains across 23 rural regions in 16 European countries. Additionally, it includes 30 JSON files that annotate the keywords within these narratives, linking them to their corresponding Wikidata entries and QIDs.

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.