In agriculture

Here are a few use cases for this project:

1. Agricultural Monitoring and Disease Management: Farmers and agricultural organizations can use the "disease cotton plant" computer vision model to monitor large-scale cotton fields for any signs of disease. Early detection and management can help in controlling the spread and minimizing the impact of diseases on the cotton crop.

2. Smart Crop Insurance: Insurance companies can provide policyholders with tailored protection by using the computer vision model to assess the health of cotton plants in a certain area. By identifying affected plants, they can offer insurance plans that accurately reflect the risk posed by diseases and other factors.

3. Agricultural Consultancy Services: Expert consultants can use the "disease cotton plant" computer vision model to advise farmers on the best methods to prevent, manage, and treat diseases affecting their cotton crops. The model can also be used for training and capacity building in disease management among extension officers and local community.

4. Research and Development: Researchers can use the model as a tool to study various aspects of cotton plant diseases, their patterns, and their impact on crop yield. This information can be valuable for creating new treatment strategies and understanding how diseases spread to improve future prevention measures.

5. Supply Chain Management: Companies dealing with cotton production can use the computer vision model to ensure the quality of sourced raw cotton material. By identifying diseased plants earlier in the supply chain, businesses can maintain a high-quality product and prevent the spread of diseases to other areas of production.

Dataset

This dataset was created by Palash S

Released under CC0: Public Domain

Contents

Annual cotton production in the United States grew from just a few thousand tons at the turn of the 19th century, to fluctuating between 1.6 million and 4.3 million tons throughout most of the 20th century. The amount of space used to produce cotton also grew from three to almost 18 million hectares of land between 1866 and the 1920s, before dropping to around four or five million hectares between the 1960s and 1980s. Despite this drop in land usage, advancements in agricultural technology meant that output remained relatively constant in the 20th century, meaning that output per hectare actually increased significantly.

The mechanical cotton gin's invention in 1793 revolutionized the U.S. cotton industry, which grew exponentially in the early 19th century. Cotton was the U.S.' primary export in these years, and its production was driven by slave labor in the southern states (particularly South Carolina). For the first time, output exceeded one million tons in 1859, and again in 1861, however, the disruption of the American Civil War caused cotton output to drop by over 93 percent in the next three years, to just 68 thousand tons by 1864. Production resumed upon its previous trajectory following the war's end, and many of the former-slaves forced to work on cotton plantations continued to work in the cotton industry, but as sharecroppers who worked the land in exchange for a share of the harvest, as well as housing and facilities (this was similar to tenant farming, although sharecroppers received a smaller share of the crop and had fewer legal protections).

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.

Cotton rose to 66.19 USd/Lbs on August 7, 2025, up 1.17% from the previous day. Over the past month, Cotton's price has risen 0.45%, and is up 0.77% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cotton - values, historical data, forecasts and news - updated on August of 2025.

Cotton root rot is a century-old cotton disease that now can be effectively controlled with Topguard Terra fungicide. Because this disease tends to occur in the same general areas within fields in recurring years, site-specific application of the fungicide only to infested areas can be as effective as and considerably more economical than uniform application. The overall objective of this research was to demonstrate how site-specific fungicide application could be implemented based on historical remote sensing imagery and using variable-rate technology. Procedures were developed for creating binary prescription maps from historical airborne and high-resolution satellite imagery. Two different variable-rate liquid control systems were adapted to two existing cotton planters, respectively, for site-specific fungicide application at planting. One system was used for site-specific application on multiple fields in 2015 and 2016 near Edroy, Texas, and the other system was used on multiple fields in both years near San Angelo, Texas. Airborne multispectral imagery taken during the two growing seasons was used to monitor the performance of the site-specific treatments. Results based on prescription maps derived from historical airborne and satellite imagery of two fields in 2015 and one field in 2016 are reported in this article. Two years of field experiments showed that the prescription maps and the variable-rate systems performed well and that site-specific fungicide treatments effectively controlled cotton root rot. Reduction in fungicide use was 41%, 43%, and 63% for the three fields, respectively. The methodologies and results of this research will provide cotton growers, crop consultants, and agricultural dealers with practical guidelines for implementing site-specific fungicide application using historical imagery and variable-rate technology for effective management of cotton root rot. Resources in this dataset: Resource Title: A ground picture of cotton root rot File Name: IMG_0124.JPG Resource Description: A cotton root rot-infested area in a cotton field near Edroy, TX. Resource Title: An aerial image of a cotton field File Name: Color-infrared image of a field.jpg Resource Description: Aerial color-infrared (CIR) image of a cotton field infested with cotton root rot. Resource Title: As-applied fungicide application data File Name: Jim Ermis-Farm 1-Field 11 Fungicide Application.csv Resource Description: As-applied fungicide application rates for variable rate application of Topguard to a cotton field infested with cotton rot

UAV Cotton Flower Counting Dataset

https://doi.org/10.5061/dryad.5qfttdzhb

Description of the data and file structure

These data were collected with a UAV at a cotton breeding field in Watkinsville, Georgia in 2021. The field was scanned twice weekly, and the data was analyzed using an automated pipeline:

• The raw images were stitched together into an orthophoto
• Individual plot-level crops were extracted
• An object detector was used to detect flowers in the plot images
• The flower counts were analyzed in order to produce various phenotyping metrics

This dataset contains the raw, annotated images used to train the object detector. In addition to the data we collected in 2021, this dataset also includes some additional data, including UAV images from previous years (2016, 2018) as well as some plot-level images collected from a tractor. All images contain bounding box annotations for each flower.

We have also included a model trai...

This dataset is about artists. It has 1 row and is filtered where the artworks is Textile sample, Cotton B 99 1. It features 9 columns including birth date, death date, country, and gender.

Dropping Ogallala aquifer levels and changing commodity prices and energy costs make irrigation management an important but uncertain issue to west Texas cotton producers. For example, is deficit or full irrigation more profitable under the current lint price and pumping cost conditions? Also, what is the best way to divide production into dryland and irrigated acreage with limited well capacity? To help producers answer these questions this web application estimates the effects of irrigation on the profitability of center pivot cotton production on the Southern High Plains. It's main purpose is to show the impact of irrigation on yield and the related effects on both profits per acre and profits over a center pivot area with combined dryland and irrigated production. Resources in this dataset:Resource Title: Cotton Irrigation Tool. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=486&modecode=30-96-05-00 download page

Methods Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image. Cotton fiber sample preparation, digital image collection, and image analysis: Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline. Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.) Resources in this dataset:Resource Title: Deltapine 90 - Manually Annotated Training Set. File Name: GH3 DP90 Keyence 1_45 JPEG.zipResource Description: These images were manually annotated in Labelbox.Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set. File Name: GH3 DP90 Keyence 46_101 JPEG.zipResource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow. Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set. File Name: GH3 DP90 Keyence 102_125 JPEG.zipResource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.Resource Title: Phytogen 800 - Evaluation Test Images. File Name: Gb cv Phytogen 800.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima 3-79 - Evaluation Test Images. File Name: Gb cv Pima 379.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima S-7 - Evaluation Test Images. File Name: Gb cv Pima S7.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Coker 312 - Evaluation Test Images. File Name: Gh cv Coker 312.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Deltapine 90 - Evaluation Test Images. File Name: Gh cv Deltapine 90.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Half and Half - Evaluation Test Images. File Name: Gh cv Half and Half.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Fiber Tip Annotations - Manual. File Name: manual_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.Resource Title: Fiber Tip Annotations - AI-Assisted. File Name: ai_assisted_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow. Resource Title: Model Weights (iteration 600). File Name: model_weights.zipResource Description: The final model, provided as a zipped Pytorch .pth file. It was chosen at training iteration 600. The model weights can be imported for use of the fiber tip type detection neural network in Python.Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

Italy Exports of cotton was US$1.1 Billion during 2024, according to the United Nations COMTRADE database on international trade. Italy Exports of cotton - data, historical chart and statistics - was last updated on August of 2025.

United States Imports from China of Cotton was US$57.26 Million during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China of Cotton - data, historical chart and statistics - was last updated on July of 2025.

China Production Price Index: AP: Farming Product: Cotton data was reported at 90.200 Prev Year=100 in Mar 2025. This records an increase from the previous number of 87.800 Prev Year=100 for Dec 2024. China Production Price Index: AP: Farming Product: Cotton data is updated quarterly, averaging 102.155 Prev Year=100 from Mar 2002 (Median) to Mar 2025, with 78 observations. The data reached an all-time high of 166.730 Prev Year=100 in Dec 2010 and a record low of 70.690 Prev Year=100 in Dec 2014. China Production Price Index: AP: Farming Product: Cotton data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Production Price Index: Quarterly.

Imports of Cotton Yarn in China decreased to 270078 USD Thousand in February from 306334 USD Thousand in January of 2024. This dataset includes a chart with historical data for China Imports of Cotton Yarn.

Burkina Faso Exports of cotton was US$335.43 Million during 2024, according to the United Nations COMTRADE database on international trade. Burkina Faso Exports of cotton - data, historical chart and statistics - was last updated on July of 2025.

This dataset is about artists. It has 1 row and is filtered where the artworks is Facade of Cotton Warehouse. It features 9 columns including birth date, death date, country, and gender.

Ghana Imports of Cotton was US$19.64 Million during 2023, according to the United Nations COMTRADE database on international trade. Ghana Imports of Cotton - data, historical chart and statistics - was last updated on July of 2025.

Understanding how much inorganic fertilizer (referred to as fertilizer) is applied to different crops at national, regional and global levels is an essential component of fertilizer consumption analysis and demand projection. Good information on fertilizer use by crop (FUBC) is rarely available because it is difficult to collect and time-consuming to process and validate. To fill this gap, a first global FUBC report was published in 1992 for the 1990/1991 period, based on an expert survey conducted jointly by the Food and Agriculture Organization (FAO) of the UN, the International Fertilizer Development Center (IFDC) and the International Fertilizer Association (IFA). Since then, similar expert surveys have been carried out and published every two to four years in the main fertilizer-consuming countries. Since 2008 IFA has led these efforts and, to our knowledge, remains the only globally available data set on FUBC. This dataset includes data (in CSV format) from a survey carried out by IFA to represent the 2017–18 period as well as a collation of all historic FUBC data. Methods Latest fertilizer use by crop survey data During 2020-2022 IFA collected and standardized FUBC data for the 2017-18 period, primarily through a survey of various country correspondents. As of May 2022 this is the most recent survey for FUBC data. Country correspondents were selected based on their knowledge for estimating fertilizer use, average fertilizer application rates and areas of crops for N, P2O5 and K2O for their respective country, and access to any locally available farm data. Country correspondents were asked to complete the questionnaire with the greatest detail possible, or to provide data for the crop breakdown available in their country. The task of aligning the data with FAO crop area statistics was particularly challenging, and sometimes impossible. Even when correspondents were able to mostly follow the provided crop breakdown, crops that are minor in a country’s agriculture were often included in a group of crops or other crops. For example, for most EU countries, the data provided by Fertilizers Europe follow the crop breakdown that is specific to their own annual survey. In this crop breakdown, rice is grouped with rye, triticale and oats, soybean is grouped with sunflower and linseed, and cotton is not identified. Historic fertilizer use by crop survey data For historic FUBC data the following sources had data manually extracted from the original pdf documents into a standardized format: · FUBC report number 1: FAO et al. (1992) · FUBC report number 2: FAO et al. (1994) · FUBC report number 3: FAO et al. (1996) · FUBC report number 4: FAO et al. (1999) · FUBC report number 5: FAO et al. (2002) · FUBC report number 6: Heffer (2009) · FUBC report number 7: Heffer (2013) · FUBC report number 8: Heffer et al. (2017) References FAO, IFA, IFDC. 1992. Fertilizer use by crop 1. Rome, Italy: Food and Agriculture Organization of the United Nations, 82 p. FAO, IFA, IFDC. 1994. Fertilizer use by crop 2. Rome, Italy: Food and Agriculture Organization of the United Nations, 64 p. FAO, IFA, IFDC. 1996. Fertilizer use by crop 3. Rome, Italy: Food and Agriculture Organisation of the United Nations, 74 p. FAO, IFA, IFDC. 1999. Fertilizer use by crop 4. Rome, Italy: Food and Agriculture Organisation of the United Nations, 78 p. FAO, IFA, IFDC, IPI, PPI. 2002. Fertilizer use by crop 5. Rome, Italy.: Food and Agriculture Organization of the United Nations, 67 p. Heffer P. 2009. Assessment of Fertilizer Use by Crop at the Global Level 2006/07 – 2007/08. Paris, France: International Fertilizer Association, 11 p. https://www.ifastat.org/consumption/fertilizer-use-by-crop. Heffer P. 2013. Assessment of Fertilizer Use by Crop at the Global Level. Paris, France, 10 p. https://www.ifastat.org/consumption/fertilizer-use-by-crop. Heffer P, Gruere A, Roberts T. 2017. Assessment of fertiliser use by crop at the global level. Paris, France: International Fertilizer Association, Institute IPN, 19 p. https://www.ifastat.org/plant-nutrition.

United States Exports of cotton to Vietnam was US$582.01 Million during 2024, according to the United Nations COMTRADE database on international trade. United States Exports of cotton to Vietnam - data, historical chart and statistics - was last updated on August of 2025.