Dataset

This dataset was created by Palash S

Released under CC0: Public Domain

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Dataset

This dataset was created by Shaik Mahmamad Rafi

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Dataset

This dataset was created by tengfei liu333

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Dataset

This dataset was created by Akash kumar

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It contains the following files:

Dataset

This dataset was created by bujo

Released under MIT

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The MegaWeeds dataset consists of seven existing datasets:

- WeedCrop dataset; Sudars, K., Jasko, J., Namatevs, I., Ozola, L., & Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control. Data in Brief, 31, 105833. https://doi.org/https://doi.org/10.1016/j.dib.2020.105833

- Chicory dataset; Gallo, I., Rehman, A. U., Dehkord, R. H., Landro, N., La Grassa, R., & Boschetti, M. (2022). Weed detection by UAV 416a Image Dataset. https://universe.roboflow.com/chicory-crop-weeds-5m7vo/weed-detection-by-uav-416a/dataset/1

- Sesame dataset; Utsav, P., Raviraj, P., & Rayja, M. (2020). crop and weed detection data with bounding boxes. https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes

- Sugar beet dataset; Wangyongkun. (2020). sugarbeetsAndweeds. https://www.kaggle.com/datasets/wangyongkun/sugarbeetsandweeds

- Weed-Detection-v2; Tandon, K. (2021, June). Weed_Detection_v2. https://www.kaggle.com/datasets/kushagratandon12/weed-detection-v2

- Maize dataset; Correa, J. M. L., D. Andújar, M. Todeschini, J. Karouta, JM Begochea, & Ribeiro A. (2021). WeedMaize. Zenodo. https://doi.org/10.5281/ZENODO.5106795

- CottonWeedDet12 dataset; Dang, F., Chen, D., Lu, Y., & Li, Z. (2023). YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture, 205, 107655. https://doi.org/https://doi.org/10.1016/j.compag.2023.107655

All the datasets contain open-field images from crops and weeds with annotations. The annotation files were converted to text files so it can be used in the YOLO model. All the datasets were combined into one big dataset with in total 19,317 images. The dataset is split into a training and validation set.

Dataset

This dataset was created by meftahul_jannat

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Dataset

This dataset was created by Pantho12

Released under Attribution 4.0 International (CC BY 4.0)

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Over 1.5K images selected from the public Kaggle DR Detection dataset; Five DR grades (DR0 / DR1 / DR2 / DR3 / DR4), re-labeled by a panel of 45 experienced ophthalmologists; Eight retinal lesion classes, including microaneurysm, intraretinal hemorrhage, hard exudate, cotton-wool spot, vitreous hemorrhage, preretinal hemorrhage, neovascularization and fibrous proliferation; Over 34K expert-labeled pixel-level lesion segments; Multi-task, i.e., lesion segmentation, lesion classification, and DR grading.

Dataset

This dataset was created by Niful Islam

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Dataset

This dataset was created by Panashe Musemwa

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Cotton Crop and Weather Relationship Dataset

This comprehensive dataset explores the intricate relationship between weather conditions and cotton crop growth over a decade (2013-2023). With over 80,000 records, it provides valuable insights into how various climatic factors influence cotton production throughout its growth cycle.

Dataset Overview:

The dataset includes the following key fields:

1. Farm identification and temporal data:

   • Farm_ID
   • Planting_Date, Harvest_Date
   • Growth_Cycle, Harvest_Year

2. Cotton yield information:

   • Yield (measured in standard units)

3. Weather conditions:

   • Sunlight_Hours
   • Precipitation
   • Average_Temperature
   • Drought_Days, Flood_Days
   • CO2_Concentration

4. Soil characteristics:

   • Soil_Moisture
   • Soil_pH

5. Calculated environmental levels:

   • Sunlight_Level
   • Flood_Level
   • Drought_Level

This rich dataset allows for in-depth analysis of how various environmental factors affect cotton growth and yield. It captures both daily weather variations and extreme events, making it valuable for studying climate change impacts on cotton farming.

Potential applications include predictive modeling of cotton yields, optimization of planting and harvesting schedules, analysis of soil condition impacts, and development of climate-resilient cotton farming strategies.

Whether you're an agronomist, data scientist, or climate researcher, this dataset provides a comprehensive resource for exploring the complex interplay between weather patterns and cotton crop performance.

Dataset

This dataset was created by Rogova Nataliya

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Description

The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircraft and drones), and ground based hyperspectral imaging spectroscopy.

The GHISA for the Conterminous United States (GHISACONUS) Version 1 product provides dominant crop data in different growth stages for various agroecological zones (AEZs) of the United States. The GHISA hyperspectral library of the five major agricultural crops (e.g., winter wheat, rice, corn, soybeans, and cotton) for CONUS was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired from 2008 through 2015 from different AEZs of CONUS using the United States Department of Agriculture (USDA) Cropland Data Layer (CDL) as reference data.

GHISACONUS is comprised of seven AEZs throughout the United States covering the major agricultural crops in six different growth stages: emergence/very early vegetative (Emerge VEarly), early and mid vegetative (Early Mid), late vegetative (Late), critical, maturing/senescence (Mature Senesc), and harvest. The crop growth stage data were derived using crop calendars generated by the Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison.

Provided in the CSV file is the spectral library including image information, geographic coordinates, corresponding agroecological zone, crop type labels, and crop growth stage labels for the United States.

Dataset

This dataset was created by Rogova Nataliya

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India is one of the major players in the agriculture sector worldwide and it is the primary source of livelihood for ~55% of India’s population. India has the world's largest cattle herd (buffaloes), largest area planted to wheat, rice, and cotton, and is the largest producer of milk, pulses, and spices in the world. It is the second-largest producer of fruit, vegetables, tea, farmed fish, cotton, sugarcane, wheat, rice, cotton, and sugar. Agriculture sector in India holds the record for second-largest agricultural land in the world generating employment for about half of the country’s population. Thus, farmers become an integral part of the sector to provide us with means of sustenance.

Consumer spending in India will return to growth in 2021 post the pandemic-led contraction, expanding by as much as 6.6%. The Indian food industry is poised for huge growth, increasing its contribution to world food trade every year due to its immense potential for value addition, particularly within the food processing industry. The Indian food processing industry accounts for 32% of the country’s total food market, one of the largest industries in India and is ranked fifth in terms of production, consumption, export and expected growth.

This data contains the production and area grown for each crop at ditrict level from 1997 to 2015.