A comprehensive lemongrass (Cymbopogon citratus) leaf dataset for agricultural research and disease prevention

A comprehensive dataset of tomato leaf images for disease analysis in Bangladesh

Agriculture is the largest employment sector in Bangladesh, making up 13.4 percent of Bangladesh's GDP in 2024. For being most consumable crops, almost 9 out of 10 farmers grow tomatoes and earn their living. Tomato (Solanum lycopersicum) ranks fourth in respect of production and third in respect of area in Bangladesh. The tomato, a cornerstone of global agriculture, relies heavily on the health of its leaves for optimal growth and yield. These leaves are essential for photosynthesis, respiration, and transpiration, processes that directly influence the plant's overall vitality. Understanding the structure, function, and physiological characteristics of tomato leaves is crucial for developing effective agricultural strategies to maximize production and minimize the impact of environmental stressors and diseases. While tomato leaves exhibit a wide range of morphological variations across cultivars, they remain susceptible to a variety of threats. Pests, pathogens, nutrient deficiencies, temperature extremes, and environmental pollutants can all compromise leaf health. Biotic stresses, especially foliar diseases caused by bacteria, fungi, viruses, and other pathogens, are particularly devastating to tomato production. This research presents a dataset of leaves from tomato plants that are both insect-damaged and healthy. Our dataset contains 1,028 images of tomato leaves collected in Bangladesh, including 482 images of healthy leaves and 546 images of diseased leaves. The images were captured from two different tomato gardens in February, 2024 [1]. We captured the images the in diverse backgrounds, angles, and lighting conditions. Each image is precisely annotated to mark regions as either healthy or diseased, accounting for the complex background in each image. This dataset serves as a comprehensive resource for researchers and learners to analyze and improve the health management of tomato plants through the development of advanced computational models.

A comprehensive hog plum leaf disease dataset for enhanced detection and classification

A comprehensive Hog plum leaf disease dataset is greatly needed for agricultural research, precision agriculture, and efficient management of disease. It will find applications toward the formulation of machine learning models for early detection and classification of disease, thus reducing dependency on manual inspections and timely interventions. Such a dataset provides a benchmark for training and testing algorithms, further enhancing automated monitoring systems and decision-support tools in sustainable agriculture. It enables better crop management, less use of chemicals, and more focused agronomical practices. This dataset will contribute to the global research being carried out for the advancement of disease-resistant plant strategy development and efficient management practices for better agricultural productivity along with sustainability. These images have been collected from different regions of Bangladesh. In this work, two classes were used: 'Healthy' and 'Insect hole', representing different stages of disease progression. The augmentation techniques that involve flipping, rotating, scaling, translating, cropping, adding noise, adjusting brightness, adjusting contrast, and scaling expanded a dataset of 3782 images to 20,000 images. These have formed very robust deep learning training sets, hence better detection of the disease.

Dataset of Centella Asiatica leaves for quality assessment and machine learning applications

Centella asiatica is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of Centella asiatica leaves are triterpenoid saponins, which are predominantly believed to be responsible for its therapeutic properties. Ensuring the use of high-quality leaves in herbal medicine preparation is crucial across all medicinal practices. To address this quality control issue using machine learning applications, we have developed an image dataset of Centella asiatica leaves. The images were captured using Samsung Galaxy M21 mobile phones and depict the leaves in "Dried," "Healthy," and "Unhealthy" states. These states are further divided into "Single" and "Multiple" leaves categories, with "Single" leaves being further classified into "Front" and "Back" views to facilitate a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images. This dataset is instrumental in the development and evaluation of image recognition algorithms, serving as a foundational resource for computer vision research. Moreover, it provides a valuable platform for testing and validating algorithms in areas such as image categorization and object detection. For researchers exploring the medicinal potential of Centella asiatica in traditional medicine, this dataset offers critical information on the plant's health, thereby advancing research in herbal medicine and ethnopharmacology.

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

This dataset presents a comprehensive collection of images representing both dried and live samples from eight distinct Thai cannabis classes. The dataset includes a total of 14,094 images, with images depicting dried and healthy specimens. These images serve as a valuable resource for researchers engaged in botanical exploration, machine learning, and computer vision studies. Additionally, the dataset facilitates investigations into the medicinal properties of Thai cannabis. Interdisciplinary collaboration is encouraged, providing opportunities for innovative insights spanning biology, horticulture, and data science. Beyond fundamental research, this dataset holds practical implications for agriculture, technology development, and disease prevention, offering insights into both dried and live states of Thai cannabis plants across various strains.