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Plant Disease Dataset

来源：花匠小妙招时间：2024-12-25 08:45

Plant Disease dataset

Leaves image dataset containing healthy and unhealthy plant leaves.

Plant Disease Expert

Image Data set for Plant Disease detection

DiaMOS Plant (A Dataset for Diagnosis and Monitoring Plant Disease)

Abstract The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.

Plant Disease Diagnosis [generalized dataset]

Real -Hand taken images of various plants (phase2) from IIIT-DM Jabalpur campus.

植物病害数据集（Plant Disease）

# Dataset This dataset was created by Saroj Raj Sharma # Contents It contains the following files:

Plant Leaf Freshness and Disease Detection Dataset From Bangladesh

This dataset contains images of widely grown crops in Bangladesh. This dataset contains images of leaf diseases and fresh leaves for 6 vegetables. The vegetables are Bitter Gourd with 2223 images, Bottle Gourd with 1803 images, Tomatoes with 2449 images, Eggplants with a total of 2944 images, Cauliflowers with 1598 images, and Cucumbers with 1626 images.

New Plant Diseases Dataset (Image dataset containing different healthy and unhealthy crop leaves.)

This dataset is recreated using offline augmentation from the original dataset. The original dataset can be found on this github repo. This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. The total dataset is divided into 80/20 ratio of training and validation set preserving the directory structure. A new directory containing 33 test images is created later for prediction purpose.

Data from: Museum specimen data reveal emergence of a plant disease may be linked to increases in the insect vector population

The emergence rate of new plant diseases is increasing due to novel introductions, climate change, and changes in vector populations, posing risks to agricultural sustainability. Assessing and managing future disease risks depends on understanding the causes of contemporary and historical emergence events. Since the mid-1990s, potato growers in the western United States, Mexico, and Central America have experienced severe yield loss from Zebra Chip disease and have responded by increasing insecticide use to suppress populations of the insect vector, the potato psyllid, Bactericera cockerelli (Hemiptera: Triozidae). Despite the severe nature of Zebra Chip outbreaks, the causes of emergence remain unknown. We tested the hypotheses that 1) B. cockerelli occupancy has increased over the last century in California and 2) such increases are related to climate change, specifically warmer winters. We compiled a dataset of 87,000 museum specimen occurrence records across the order Hemiptera collected between 1900 and 2014. We then analyzed changes in B. cockerelli distribution using a hierarchical occupancy model using changes in background species lists to correct for collecting effort. We found evidence that B. cockerelli occupancy has increased over the last century. However, these changes appear to be unrelated to climate changes, at least at the scale of our analysis. To the extent that species occupancy is related to abundance, our analysis provides the first quantitative support for the hypothesis that B. cockerelli population abundance has increased, but further work is needed to link B. cockerelli population dynamics to Zebra Chip epidemics. Finally, we demonstrate how this historical macro-ecological approach provides a general framework for comparative risk assessment of future pest and insect vector outbreaks.

Zizania and Apple Image Dataset

The zizania image dataset consists of a total of 4900 zizanias. The quantity of high quality samples is 2648 and defective quality samples is 2252. There are four classes in the apple image dataset, which are apples with a diameter greater than 90 mm, between 80 mm and 90 mm, less than 80 mm, and diseases and insect pests. The quantity distributionin above categories are 3647 (51.19%), 2464 (34.59%), 558 (7.83%), 455 (6.39%).

ktennyson6/save2

该数据集包含图像、描述和关系三个特征。数据集分为训练集，包含6237个样本，占用了981790431.631字节的存储空间。数据集的总下载大小为851998201字节，总大小为981790431.631字节。

Rice Plant diseases dataset

Common Leaf diseases in Rice Plant

Mango Plant Disease

Mango leaf images classified into different disease classes

Insect Vectors of Plant Disease

This is a database of Insect Vectors of Plant Disease worldwide. The database contains ~250 records of Auchenorrhyncha species (leafhoppers, planthoppers, treehoppers, froghoppers, spittlebugs) confirmed to be vectors of plant pathogens. Taxonomy, global distributions, species descriptions and plant pathogen relationships are included for each vector species. The website is available at https://Insectvectors.science. A RESTful Data APi is provided at https://Insectvectors.science/api

Plant_Leaf_Diseases

Dataset of Plant Leaf Diseases images and corresponding labels

new-plant-diseases-dataset

The dataset consists of images of both healthy and unhealthy crop leaves.

khushwant04/Plant-Disease-Dataset

--- license: apache-2.0 ---

Edge effects, not connectivity, determine the incidence and development of a foliar fungal plant disease

Using a model plant-pathogen system in a large-scale habitat corridor experiment, we found that corridors do not facilitate the movement of wind-dispersed plant pathogens, that connectivity of patches does not enhance levels of foliar fungal plant disease, and that edge effects are the key drivers of plant disease dynamics. Increased spread of infectious disease is often cited as a potential negative effect of habitat corridors used in conservation, but the impacts of corridors on pathogen movement have never been tested empirically. Using sweet corn (Zea mays) and southern corn leaf blight (Cochliobolus heterostrophus) as a model plant-pathogen system, we tested the impacts of connectivity and habitat fragmentation on pathogen movement and disease development at the Savannah River Site, South Carolina, USA. Over time, less edgy patches had higher proportions of diseased plants, and distance of host plants to habitat edges was the greatest determinant of disease development. Variation in average daytime temperatures provided a possible mechanism for these disease patterns. Our results show that worries over the potentially harmful effects of conservation corridors on disease dynamics are misplaced, and that, in a conservation context, many diseases can be better managed by mitigating edge effects.

Appendix B. List of host plant species affected by smut and rust diseases and studies involving plant pathogens in Neotropical forests.

List of host plant species affected by smut and rust diseases and studies involving plant pathogens in Neotropical forests.

Additional file 5 of A simplified synthetic community rescues Astragalus mongholicus from root rot disease by activating plant-induced systemic resistance

Additional file 4: Table S3. Root and rhizosphere community KEGG pathways.

Cricket Legends Image Dataset

Image collection of 30 greatest cricketers of all time