Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

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.

Coconut (Cocos nucifera) tree disease dataset: A dataset for disease detection and classification for machine learning applications.

The ``Coconut (Cocos nucifera) Tree Disease Dataset'' comprises 5,798 images across five disease categories: ``Bud Root Dropping,'' ``Bud Rot,'' ``Gray Leaf Spot,'' ``Leaf Rot,'' and ``Stem Bleeding.'' This dataset is intended for machine learning applications, facilitating disease detection and classification in coconut trees. The dataset's diversity and size make it suitable for training and evaluating disease detection models. The availability of this dataset will support advancements in plant pathology and aid in the sustainable management of coconut plantations. By providing a valuable resource for researchers, this dataset contributes to improved disease management and sustainable coconut plantation practices.