Empowering agricultural research: A comprehensive custard apple (Annona squamosa) disease dataset for precise detection.

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants. Utilizing methods like deep learning, feature extraction, and pattern recognition, this dataset sharpens automated disease identification precision. Its extensive range suits testing and training disease identification techniques. Public access fosters collaboration, fast-tracking plant pathology advancements and supporting Custard Apple plant sustainability. This dataset fosters collaborative efforts, aiding disease prevention techniques to boost Custard Apple yield and refine farming. It enhances disease identification, monitoring, and management in Custard Apple production, aiming to elevate agricultural practices and crop yields.

Mint leaves: Dried, fresh, and spoiled dataset for condition analysis and machine learning applications.

We present a comprehensive dataset of 5,323 images of mint (pudina) leaves in various conditions, including dried, fresh, and spoiled. The dataset is designed to facilitate research in the domain of condition analysis and machine learning applications for leaf quality assessment. Each category of the dataset contains a diverse range of images captured under controlled conditions, ensuring variations in lighting, background, and leaf orientation. The dataset also includes manual annotations for each image, which categorize them into the respective conditions. This dataset has the potential to be used to train and evaluate machine learning algorithms and computer vision models for accurate discernment of the condition of mint leaves. This could enable rapid quality assessment and decision-making in various industries, such as agriculture, food preservation, and pharmaceuticals. We invite researchers to explore innovative approaches to advance the field of leaf quality assessment and contribute to the development of reliable automated systems using our dataset and its associated annotations.