A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per mm.

The relationship between the plants and the environment is multitudinous and complex. They help in nourishing the atmosphere with diverse elements. Plants are also a substantial element in regulating carbon emission and climate change. But in the past, we have destroyed them without hesitation. For the reason that not only we have lost a number of species located in them, but also a severe result has also been encountered in the form of climate change. However, if we choose to give them time and space, plants have an astonishing ability to recover and re-cloth the earth with varied plant and species that we have, so recently, stormed. Therefore, a contribution has been made in this work towards the study of plant leaf for their identification, detection, disease diagnosis, etc. Twelve economically and environmentally beneficial plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected for this purpose. Leaf images of these plants in healthy and diseased condition have been acquired and alienated among two separate modules.

Principally, the complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. We have collected about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. All the leaf images were collected from the Shri Mata Vaishno Devi University, Katra. This process has been carried out form the month of March to May in the year 2019. The images are captured in a closed environment. This acquisition process was completely wi-fi enabled. All the images are captured using a Nikon D5300 camera inbuilt with performance timing for shooting JPEG in single shot mode (seconds/frame, max resolution) = 0.58 and for RAW+JPEG = 0.63. The images were in .jpg format captured with 18-55mm lens with sRGB color representation, 24-bit depth, 2 resolution unit, 1000-ISO, and no flash.

Further, we hope that this study can be beneficial for researchers and academicians in developing methods for plant identification, plant classification, plant growth monitoring, leave disease diagnosis, etc. Finally, the anticipated impression is towards a better understanding of the plants to be planted and their suitable management.

PlantDoc is a dataset for visual plant disease detection. The dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images.

The dataset WaRP (Waste Recycling Plant) includes labeled images of an industrial waste sorting plant. We have selected 28 recyclable waste categories. Objects in the dataset are divided into the following groups: plastic bottles of 17 categories (class name with the bottle- prefix), glass bottles of three types (the glass- prefix), card boards of two categories, detergents of four categories, canisters and cans. The -full postfix means that the bottle is filled with air, i.e. not flat.

Link for related paper: https://www.sciencedirect.com/science/article/abs/pii/S0952197623017268

Three types of models can be learned over the WaRP dataset: detection, classification and segmentation. See details in our EAAI Paper

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5643117%2F4291f7a0bfa4e85de656eb185ae6b4c7%2FWaRP-Dataset.png?generation=1680273407119852&alt=media" alt="">

Warp-D Detection The key dataset part WaRP-D contains 2452 images in the training sample and 522 images in the validation sample. The images have full HD resolution of 1920 × 1080 pixels. Each image has .txt annotation with bboxes.

Warp-C Classification WaRP-C is cut-out image areas from the WaRP-D set with class labels. This part includes 8823 images for training and 1583 for testing. The images range in size from 40 to 703 pixels wide and 35 to 668 pixels high. The dataset is unbalanced because iof the real conditions of an industrial enterprise. The rarest class is the bottle-oil-full (air-filled plastic sunflower oil bottles) category, which includes only 32 crops. The most common category is bottle-transp (transparent bottles), with 1667 clipped images.

Warp-S Segmentation WaRP-S contains a total of 112 images ranging in size from 100 × 96 pixels to 412 × 510 pixels, each category has 4 images with significantly deformed recyclable objects.

Citing Please consider citing the following paper in any research manuscript using the WaRP Dataset:

@article{yudin2024hierarchical, title={Hierarchical waste detection with weakly supervised segmentation in images from recycling plants}, author={Yudin, Dmitry and Zakharenko, Nikita and Smetanin, Artem and Filonov, Roman and Kichik, Margarita and Kuznetsov, Vladislav and Larichev, Dmitry and Gudov, Evgeny and Budennyy, Semen and Panov, Aleksandr}, journal={Engineering Applications of Artificial Intelligence}, volume={128}, pages={107542}, year={2024}, publisher={Elsevier} }

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.Many of the investigator working on disease detection in Basil leaves where the following diseases occur 1) Gray Mold 2) Basal Root Rot, Damping Off 3) Fusarium Wilt and Crown Rot4) Leaf Spot5) Downy MildewThe Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo, TensorFlow, OpenCV, deep learning, CNNI had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

Dataset

This dataset was created by Dwi Purwanto

Contents

Here are a few use cases for this project:

1. Agricultural disease and pest monitoring: The "plant" model could be used to monitor crops for early signs of disease or pest infestations by identifying specific leaf conditions, enabling farmers to apply timely treatments and prevent damage to large portions of their crops.

2. Plant care and maintenance: Gardeners and horticulture enthusiasts could use the "plant" model to diagnose the health of their plants and determine necessary treatment or care methods, leading to better maintenance and improved plant growth.

3. Botanical research and education: Researchers and educators can use the "plant" model as a teaching tool to help students identify and study various plant leaves and their diseases, promoting a deeper understanding of botany and plant pathology.

4. Plant identification and biodiversity tracking: Environmental scientists and nature enthusiasts can utilize the "plant" model to identify plant species in a given area. This can contribute to research on biodiversity, conservation efforts, and the tracking of invasive species.

5. Horticulture industry quality control: The "plant" model could be used by horticulture industry professionals to monitor plant quality and diagnose potential diseases, allowing them to maintain high standards and address issues proactively to keep consumers satisfied with healthy, attractive plants.

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control.

Original Title: Concentrations of plant growth promoting compounds in soils and hydroponics due to the interaction of plants and earthworms

Original Description: Concentrations of plant growth promoting compounds in soils and hydroponics due to the interaction of plants and earthworms

Data comprise phytohormone concentrations (plant growth hormones: adenosine, zeatin, isopentenyladenosine, indole-3-acetic acid and abscisic acid) measured during plant growth experiments in soil and hydroponic growth media in the presence and absence of earthworms (Lumbricus terrestris and Eisenia fetida respectively). Also presented are plant biomass, pH of the hydroponic solution and soil biological activity (concentration of Fluorescein diacetate - a measure of the hydrolytic capacity) at the end of the study. The study was funded by the NERC (Grant number NE/M000648/1). Mass spectrometry was carried out in The York Centre of Excellence in Mass Spectrometry; the centre was created thanks to a major capital investment through Science City York, supported by Yorkshire Forward with funds from the Northern Way Initiative, and subsequent support from EPSRC (EP/K039660/1; EP/M028127/1). Full details about this dataset can be found at https://doi.org/10.5285/809cd6e8-0615-45ff-b79b-6ba1ae474713

Source: https://ckan.publishing.service.gov.uk/dataset/concentrations-of-plant-growth-promoting-compounds-in-soils-and-hydroponics-due-to-the-interact
Last updated at https://ckan.publishing.service.gov.uk/dataset : 2020-03-06

Plant-pollinator interactions database derived from biological recording data, unpublished experimental data and published interactions in books and papers. The database covers all recorded interactions for bees, hoverflies and butterflies in mainland GB. Interactions were inferred from biological recording metadata by algorithmically screening for text matching a valid scientific or vernacular plant name (or a widely used synonym or abbreviation of either), followed by manual data cleaning. These data were compiled for the construction of multiple potential plant-pollinator networks in combination with plant and pollinator occurrence data. Full details about this dataset can be found at https://doi.org/10.5285/6d8d5cb5-bd54-4da7-903a-15bd4bbd531b

The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.

NOTE: The original dataset is not available from the original source (plantvillage.org), therefore we get the unaugmented dataset from a paper that used that dataset and republished it. Moreover, we dropped images with Background_without_leaves label, because these were not present in the original dataset.

Original paper URL: https://arxiv.org/abs/1511.08060 Dataset URL: https://data.mendeley.com/datasets/tywbtsjrjv/1

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('plant_village', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/plant_village-1.0.2.png" alt="Visualization" width="500px">

envedabio/plant-phenotype dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

Of the three major crops – rice, wheat and maize – rice is by far the most important food crop for people in low- and lower-middle-income countries. Although rich and poor people alike eat rice in low-income countries, the poorest people consume relatively little wheat and are therefore deeply affected by the cost and availability of rice.

In many Asian countries, rice is the fundamental and generally irreplaceable staple, especially of the poor. For the extreme poor in Asia, who live on less than $1.25 a day, rice accounts for nearly half of their food expenditures and a fifth of total household expenditures, on average. This group alone annually spends the equivalent of $62 billion (purchasing power parity) on rice. Rice is critical to food security for many of the world’s poor people.

~ Quote from ricepedia.org

Content

This dataset contains 120 jpg images of disease infected rice leaves. The images are grouped into 3 classes based on the type of disease. There are 40 images in each class.

Classes

• Leaf smut
• Brown spot
• Bacterial leaf blight

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1086574%2F440c2d8d39025fc8be9929836686cbc1%2Frice_leaves.png?generation=1582347404740337&alt=media" alt="">

This dataset is associated with the following paper: Detection and Classification of Rice Plant Diseases

The authors gathered these leaves from a rice field in a village called Shertha in Gujarat, India.

Citation

Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intelligent Decision Technologies. 2017 Jan 1;11(3):357-73, doi: 10.3233/IDT-170301.

UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases#

Acknowledgements

Many thanks to the research team at the Department of Information Technology, Dharmsinh Desai University for making this dataset publicly available.

Inspiration

• Build a dataset like this that includes more types of rice leaf diseases. Collect samples of both healthy and disease infected rice leaves from a farming community. Label the dataset using information from local farmers or from plant pathologists.
• Build a model to automatically classify rice leaf diseases.
• Deploy your model as a Tensorflow.js web app so it can be accessed from anywhere in the world.
• Plantix is an excellent example of an impactful agtech mobile app. This video has more info.

Header image by HoangTuan_photography on Pixabay.

The present Article , a plant base Miccell (PBMC) has been proposed as a new electrical energy source to power low power consumption devices such as a transmitter. The PBMC constitutes of a power management system that is connected to Cu-Zn electrode pairs which are embedded into the leaves of the Aloe Vera plants. The proposed power management system can perform a fully autonomous operation to harvest the electrical energy from the Aloe Vera plants to trigger a transmitter load to send signal periodically to the temperature and humidity sensor. This has been confirmed by performing the experiment under a real-life condition. The designed power management circuit, which consists of an energy storage system and a voltage regulation system, can store the minute energy harvested from the Aloe Vera plants and boost them into sufficient energy to power a transmitter load. The transmitter load is proven to be in operation as it sends an intermittent signal to the receiver circuit to activate a remote sensor to measure the surrounding temperature and humidity. Thus, it is experimentally proven in this paper that Aloe Vera plants can be used as an energy source to provide electrical energy and its combination with the proposed power management circuit can act as a plant base cell. The idea of the proposed plant as a battery source can provide significant benefits in IoT application especially in remote areas or dense forest where replacing battery or recharging battery is impossible. The proposed cell can also be employed for precision farming and environmental monitoring where plants are available in abundant.Proposes a design of a power management circuit that can harness, store and manage the electrical energy which is harvested from the leaves of (Aloe Vera) plants to trigger a transmitter load to power a remote sensor. In the present paper, we have proposed a power management circuit, which can harvest the electrical energy from the Aloe Vera plants and converts the plants into a plant-based cell (PBC) to activate a remote sensor via a wireless transmission.The power management circuit consists of two sections namely; an energy storage system that acts as an energy storage reservoir to store the energy harvested from the plants as well as a voltage regulation system which is used to boost and manage the energy in accordance to a load operation.

Following an Order Instituting Rulemaking initiated in October 2005, amendments adopted by the Energy Commission and approved by California's Office of Administrative Law in July 2007 created two articles: Article 1, known as Quarterly Fuel and Energy Report (QFER) directed at current California energy information, and Article 2 directed at the forecast and assessment of energy loads and resources. The regulations under QFER provide for the collection of energy data relating to electric generation, control area exchanges, and natural gas processing and deliveries. The reports are submitted on forms specified by the Energy Commission's executive director.

The statistics presented here are derived from the QFER CEC-1304 Power Plant Owner Reporting Form. The CEC-1304 reporting form collects data from power plants with a total nameplate capacity of 1MW or more that are located within California or within a control area with end users inside California. The information includes gross generation, net generation, fuel use by fuel type for each generator, as well as total electricity consumed on site and electricity sales for the plant as a whole. Power plants with nameplate capacity of 20 megawatts or more also provide environmental information related to water supply and water/wastewater discharge.

Database and Source Files updated: June 07, 2017

The Plant RNA-Image Repository is a compiled database of plant images and omics data. The dataset contains images of four distinct plant maladies, including powdery mildew, rust, leaf spot, and blight, as well as gene expression and metabolite data. Using a high-resolution camera in a controlled environment at the facility of the various Agriculture Universities of the Khyber Pakhtunkhwa, Pakistan. We captured 26940 images of plants, where each class has different number of samples for each disease type. Each image was labelled with the disease type corresponding to it. The images were preprocessed by resizing them to 224x224 pixels and standardizing the pixel values. In addition to collecting images of the same plants, we also collected gene expression and metabolite data. We extracted RNA from the plant leaves using a commercial reagent and sequenced it on an Illumina HiSeq 4000 platform. The average length of the 100 million pairedend readings obtained was 150 base pairs. The unprocessed reads were trimmed with Trimmomatic and aligned with STAR against the reference genome. We counted the number of reads that mapped to each gene using featureCounts, and then identified differentially expressed genes between healthy and diseased plants using the DESeq2 package in R. Using gas chromatography-mass spectrometry (GC-MS), we gathered additional metabolite information. Using a methanol-water extraction protocol, we extracted metabolites from the plant leaves and analyzed the extracts using GC-MS.

The dataset contains aerial agricultural images of a potato field with manual labels of healthy and stressed plant regions. The images were collected with a Parrot Sequoia multispectral camera carried by a 3DR Solo drone flying at an altitude of 3 meters. The dataset consists of RGB images with a resolution of 750×750 pixels, and spectral monochrome red, green, red-edge, and near-infrared images with a resolution of 416×416 pixels, and XML files with annotated bounding boxes of healthy and stressed potato crop.

The Existing Hydropower Assets (EHA) Plant Database is a geospatially comprehensive point-level dataset containing locations and key characteristics of U.S. hydropower plants that are currently operational.

Plants are one of the most important species on earth. Its role in the ecosystem, preventing natural disasters, and ingredients of many medicines are the reasons for protecting this species. The amount of biodiversity in plants is one of the challenges to maintaining biodiversity conservation. Therefore, we need a way to protect biodiversity.

The first step that can be done is to identify plant species. By knowing the identity of the plant, information about the type, origin, benefits, and methods of conservation of the plant can be known later. Plants have several parts, such as roots, stems, fruits, flowers, and leaves. Identification of plants based on their leaves is more efficient because the leaves are almost there at all times, are easier to reach, and cause minor damage to the plant if the leaves are picked. In this dataset, there are pictures of tropical leaves, especially those that can grow in Indonesia. Indonesia was chosen because Indonesia is one of the countries with the greatest biodiversity. Indonesia is also one of the countries that have tropical rain forests. There are ten plant species contained in this dataset, such as Averrhoa bilimbi (Blimbing Wuluh), Psidium guajava (Jambu Biji), Citrus Aurantiifolia (Jeruk Nipis), Ocimum Africanum (Kemangi), Aloe vera (Lidah Buaya), Artocarpus heterophyllus (Nangka), Pandanus Amaryllifolius (Pandan), Carica papaya (Pepaya), Apium graveolens (Seledri), Piper Betle (Sirih). The total dataset is 3500 images. Each species has 350 high-resolution images. Folders are named according to names in Indonesian. Each image has a white background. The format for all images is .jpg. In addition, the dimension of each image is 1600 x 1200. The Indonesian Herb Leaf Dataset (IHLD) can be used to develop plant identification models using artificial intelligence. By releasing this dataset to the public, we look forward to stimulating research and adding content to the existing leaf datasets.