Comprehensive database of marijuana prices across legal U.S. states, including both recreational and medical cannabis markets.

Printouts of detailed topographic maps showing infestation of invasive plant species along the coastal side of the Santa Lucia Range from Big Sur to the Monterey County-San Luis Obispo County line. Covers the following invasive plants: cape ivy (Delairia odorata), French broom (Genista monspessulana), eupatory (Ageratina adenophora), pampas grass (Cortaderia selloana), Italian thistle (Carduus pycnocephalus), ice plant (Carpobrotus edulis), yellow star thistle (Centaura solstitialis), giant reed (Arundo donax). Includes percent cover shown through 4 classifications. Jubata Grass Inventory: A GIS mapping of 51 jubata grass (Cortaderia jubata) infestations with aspect, slope, soil type, density of stand, associated plant species, and canopy of associated species documented. This study showed that pampas grass infestations occur in a very particular environment along the Big Sur coast: Rock Outcrop-Xerorthents Association (Rc) soil with little or no canopy cover from associated species.

Weed Identification

## Overview

Weed Identification is a dataset for object detection tasks - it contains Plants annotations for 322 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

WIKTROP is a geographical extension of WIKWIO portal to tropical and mediterranean areas around the world. It aims to strengthen science and technology orientation to achieving food security by enhancing agricultural productivity in the tropical and mediterranean areas.

Weed Identification 2

## Overview

Weed Identification 2 is a dataset for object detection tasks - it contains Weed annotations for 871 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The District of Columbia holds the record for the highest price per ounce of high quality marijuana in the United States, as of January 2025, with an average price of some 590 U.S. dollars per ounce. Recreational cannabis use Recreational cannabis is not legal in all U.S. states; however, many consumers use the drug illicitly. A recent survey indicated that over half of U.S. adults think that cannabis should be legalized and taxed like alcohol and tobacco. Recreational cannabis is often used by consumers for relaxation, stress relief, and creativity, to name a few. Surveys have shown that a majority of recreation users use marijuana for relaxation. Legal marijuana market potential It is projected that by 2025 the sales of legal cannabis in the U.S. will generate approximately 25 billion dollars in revenue. The regulation of marijuana includes the taxation of all sales. There is potential for the U.S. cannabis market to generate significant taxes and boost the economy. It is estimated that the taxes from legal marijuana sales would exceed the taxes earned from U.S. sales taxes by a significant amount.

AI Weed Identification App Market Outlook

According to our latest research, the global AI Weed Identification App market size stood at USD 312 million in 2024 and is expected to reach USD 1.28 billion by 2033, growing at a robust CAGR of 17.2% during the forecast period. The market’s impressive growth trajectory is driven primarily by the increasing adoption of precision agriculture technologies and the urgent need for sustainable weed management solutions. The proliferation of smartphones, advancements in artificial intelligence, and heightened awareness among farmers regarding crop yield optimization are further catalyzing the expansion of this market. As per our latest research, the integration of AI with digital agriculture is transforming weed identification and control practices globally.

One of the principal growth factors for the AI Weed Identification App market is the escalating demand for improved crop productivity and cost-effective weed management. Traditional weed control methods, such as manual scouting and blanket herbicide application, are labor-intensive, time-consuming, and often result in excessive chemical usage. AI-powered weed identification apps offer a precise alternative by enabling real-time weed detection, species classification, and targeted herbicide application. This not only reduces operational costs for farmers but also minimizes environmental impact, aligning with the growing emphasis on sustainable agriculture. The rapid adoption of smartphones and high-speed internet connectivity in rural areas further facilitates the widespread use of these applications, making them accessible to a broader user base.

Another significant driver is the continuous advancement in artificial intelligence, particularly in computer vision and deep learning algorithms. These technological improvements have greatly enhanced the accuracy and reliability of weed identification apps, allowing them to distinguish between crops and a wide variety of weed species under diverse field conditions. AI-based apps can now process images captured via mobile devices or drones, providing instant analysis and actionable insights. This capability is particularly valuable in large-scale commercial farming, where timely and accurate weed detection can significantly impact overall yield and profitability. Moreover, the integration of these apps with other digital agriculture platforms, such as farm management systems and geospatial mapping tools, is creating a comprehensive ecosystem for precision weed management.

Government initiatives and regulatory support are also playing a pivotal role in propelling the AI Weed Identification App market forward. Many governments and agricultural organizations worldwide are promoting the adoption of digital technologies to enhance food security and environmental sustainability. Subsidies, training programs, and awareness campaigns are encouraging farmers and agronomists to embrace AI-driven weed management solutions. Additionally, the increasing focus on reducing chemical residues in food products and preserving soil health is prompting stakeholders across the agricultural value chain to invest in innovative weed control technologies. These collective efforts are fostering a conducive environment for the growth and adoption of AI weed identification apps.

From a regional perspective, North America currently leads the AI Weed Identification App market, accounting for the largest share in 2024. This dominance is attributed to the presence of advanced agricultural infrastructure, high technology adoption rates, and strong government support for precision farming initiatives. Europe follows closely, driven by stringent environmental regulations and a well-established research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization in agriculture, increasing smartphone penetration, and substantial investments in agri-tech startups. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as awareness and infrastructural capabilities continue to improve.

Weed Identification AI Market Outlook

According to our latest research, the global Weed Identification AI market size reached USD 412.7 million in 2024, with a robust year-on-year growth trajectory. The market is anticipated to expand at a CAGR of 18.6% during the forecast period, propelling the industry to a projected value of USD 1,964.2 million by 2033. This impressive growth is primarily driven by the increasing adoption of artificial intelligence in precision agriculture, the surge in demand for sustainable farming practices, and advancements in computer vision technologies for real-time weed detection and management. As per our latest research, the integration of AI-based solutions into weed identification processes is revolutionizing the way farmers and agronomists approach crop management, significantly improving yield outcomes and reducing operational costs.

The most significant growth factor for the Weed Identification AI market is the escalating need for precision agriculture to optimize crop yields while minimizing environmental impact. Farmers and agricultural enterprises are increasingly utilizing AI-powered weed identification systems to distinguish between crops and various weed species, enabling targeted herbicide application and reducing chemical usage. The integration of advanced machine learning algorithms and high-resolution imaging technologies has made weed detection more accurate and efficient than ever before. This not only supports sustainable farming by minimizing herbicide resistance and soil degradation but also leads to substantial cost savings for farmers. The push towards digital transformation in agriculture, combined with government initiatives promoting smart farming, is further accelerating the adoption of weed identification AI solutions globally.

Another key driver is the rapid advancement in AI hardware and software capabilities, which has significantly enhanced the performance and scalability of weed identification systems. Modern AI models can process vast amounts of image data in real-time, allowing for continuous monitoring and immediate intervention across large agricultural fields. The proliferation of edge computing devices and the availability of cloud-based platforms have democratized access to sophisticated AI tools, making them affordable for small and medium-sized farms as well as large agricultural enterprises. Additionally, growing investments from agri-tech startups and established technology companies are fueling innovation, resulting in the development of user-friendly interfaces and integration with other farm management systems. These technological advancements are reducing barriers to entry and expanding the addressable market for weed identification AI solutions.

The increasing focus on sustainable agriculture and regulatory pressure to limit the use of chemical herbicides are also shaping the growth trajectory of the Weed Identification AI market. Environmental concerns related to herbicide runoff, biodiversity loss, and soil health have prompted policymakers to advocate for precision weed management practices. AI-driven weed identification systems enable compliance with these regulations by facilitating precise and minimal chemical application. Furthermore, consumer demand for organic and sustainably produced food products is encouraging farmers to adopt AI-based solutions that support eco-friendly farming practices. The alignment of market trends with evolving regulatory frameworks is creating a favorable environment for the widespread adoption of weed identification AI technologies across diverse agricultural landscapes.

From a regional perspective, North America currently leads the global Weed Identification AI market, driven by high technology adoption rates, strong research and development infrastructure, and the presence of major agri-tech companies. Europe follows closely, benefiting from supportive government policies and significant investments in sustainable agriculture. The Asia Pacific region is emerging as a high-growth market, fueled by the modernization of agriculture, increasing farm mechanization, and a large base of smallholder farmers seeking cost-effective AI solutions. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding agricultural activities and rising awareness of the benefits of precision farming. Each region presents unique opportunities and challenges, but the overarching trend is a

This dataset were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. And acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR). Images contains bean, with various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) The ground truth is defined for each images with polygons around leafs boundaries: In addition, each polygons are labeled into crop or weed.

Current prices and price indexes of cannabis, by medical purposes and non-medical purposes, Canada, provinces and territories, annual.

AI Weed Identification Robot Market Outlook

According to our latest research, the AI Weed Identification Robot market size reached USD 1.42 billion globally in 2024, demonstrating strong momentum as advanced automation technologies permeate the agricultural sector. The market is exhibiting a robust CAGR of 18.7% from 2025 to 2033, and is forecasted to attain a value of USD 6.91 billion by 2033. This remarkable growth is primarily fueled by the rising demand for precision agriculture, labor shortages in farming, and the increasing adoption of artificial intelligence and robotics to optimize crop yields and sustainability.

One of the principal growth drivers for the AI Weed Identification Robot market is the escalating global need for sustainable agricultural practices. As the world’s population continues to rise, food security has become a top priority, compelling stakeholders to seek innovative solutions for maximizing crop output while minimizing environmental impact. AI-powered weed identification robots offer a transformative approach by precisely targeting weeds, thereby reducing the use of herbicides and promoting healthier soil. These robots leverage advanced machine learning and computer vision algorithms to accurately distinguish between crops and weeds, ensuring effective weed management with minimal collateral damage. The integration of these systems into modern farms not only enhances productivity but also aligns with global sustainability goals, making them highly attractive to both large-scale commercial farms and smaller agricultural enterprises.

Another significant factor propelling the market is the acute shortage of labor in the agricultural sector, particularly in developed regions such as North America and Europe. Traditional manual weed control methods are labor-intensive and increasingly impractical due to rising wage costs and an aging rural workforce. The deployment of AI weed identification robots addresses these challenges by automating repetitive and strenuous tasks, thereby reducing dependency on human labor and lowering operational costs. Furthermore, these robots can operate continuously with high accuracy, leading to improved efficiency and consistency in weed management. The growing awareness among farmers regarding the long-term cost benefits and the potential for increased yields is accelerating the adoption of these intelligent robotic solutions.

Technological advancements are also playing a pivotal role in shaping the AI Weed Identification Robot market. The fusion of machine learning, computer vision, and sensor technologies has enabled the development of highly sophisticated robots capable of real-time weed detection and removal. Innovations in sensor fusion and data analytics are allowing these robots to adapt to varying field conditions and crop types, further expanding their applicability across different agricultural domains. In addition, the proliferation of cloud computing and IoT connectivity is facilitating remote monitoring and management of these robots, providing farmers with actionable insights to optimize their operations. As the technology matures and becomes more accessible, the market is expected to witness a surge in adoption across both developed and emerging economies.

From a regional perspective, North America currently dominates the AI Weed Identification Robot market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The high adoption rate in North America is attributed to the presence of technologically advanced commercial farms, substantial investments in agri-tech innovations, and supportive government initiatives promoting sustainable farming. Europe’s strong focus on environmental conservation and precision agriculture is also driving significant market growth, while the Asia Pacific region is emerging as a lucrative market due to increasing mechanization in agriculture and growing awareness of smart farming solutions. As these trends continue, regional dynamics are expected to evolve, with Asia Pacific anticipated to exhibit the fastest CAGR through 2033.

BackgroundThe recent exponential increase in legalized medical and recreational cannabis, development of medical cannabis programs, and production of unregulated over-the-counter products (e.g., cannabidiol (CBD) oil, and delta-8-tetrahydrocannabinol (delta-8-THC)), has the potential to create unintended health consequences. The major cannabinoids (delta-9-tetrahydrocannabinol and cannabidiol) are metabolized by the same cytochrome P450 (CYP) enzymes that metabolize most prescription medications and xenobiotics (CYP3A4, CYP2C9, CYP2C19). As a result, we predict that there will be instances of drug-drug interactions and the potential for adverse outcomes, especially for prescription medications with a narrow therapeutic index.MethodsWe conducted a systematic review of all years to 2023 to identify real world reports of documented cannabinoid interactions with prescription medications. We limited our search to a set list of medications with predicted narrow therapeutic indices that may produce unintended adverse drug reactions (ADRs). Our team screened 4,600 reports and selected 151 full-text articles to assess for inclusion and exclusion criteria.ResultsOur investigation revealed 31 reports for which cannabinoids altered pharmacokinetics and/or produced adverse events. These reports involved 16 different Narrow Therapeutic Index (NTI) medications, under six drug classes, 889 individual subjects and 603 cannabis/cannabinoid users. Interactions between cannabis/cannabinoids and warfarin, valproate, tacrolimus, and sirolimus were the most widely reported and may pose the greatest risk to patients. Common ADRs included bleeding risk, altered mental status, difficulty inducing anesthesia, and gastrointestinal distress. Additionally, we identified 18 instances (58%) in which clinicians uncovered an unexpected serum level of the prescribed drug. The quality of pharmacokinetic evidence for each report was assessed using an internally developed ten-point scale.ConclusionDrug-drug interactions with cannabinoids are likely amongst prescription medications that use common CYP450 systems. Our findings highlight the need for healthcare providers and patients/care-givers to openly communicate about cannabis/cannabinoid use to prevent unintended adverse events. To that end, we have developed a free online tool (www.CANN-DIR.psu.edu) to help identify potential cannabinoid drug-drug interactions with prescription medications.

An Image dataset consisting of weeds in multiple formats to advance computer vision algorithms for real-time weed identification and spot spraying application.

Every dataset in Weed-AI includes imagery of crops or pasture with weeds annotated, and is available in an MS-COCO derived format with standardised agricultural metadata.

The spot price for wholesale cannabis in the United States has fluctuated over the past three years. In January 2024, the spot price of wholesale cannabis was ***** U.S. dollars per pound.

Zip codes identified by the Arizona Department of Health Services to promote ownership of marijuana establishments in communities disproportionately affected by the enforcement of Arizona’s previous marijuana laws under Prop 207 (passed by Arizona voters in Nov. 2020).In December 2021, the Arizona Department of Health Services will issue 26 adult-use Marijuana Establishment licenses to applicants who qualify under the social equity ownership program. More information about the State of Arizona's Medical Marijuana Social Equity Ownership Program can be found here: https://azdhs.gov/licensing/marijuana/social-equity/index.php To see if you quality go to the map on this page: https://www.azdhs.gov/licensing/marijuana/social-equity/index.php#qualification The source data for this data layer was the Census 2020 Zip Code Tabulation Areas (ZCTAs). Zip codes for the Medical Marijuana Social Equity Ownership Program were selected by the Arizona Department of Health Services, which were then identified in the ZCTA 2020 GIS layer.

Drone-Assisted Pasture Weed Identification Market Outlook

According to our latest research, the global drone-assisted pasture weed identification market size reached USD 1.12 billion in 2024, supported by a robust compound annual growth rate (CAGR) of 18.7%. This growth is primarily driven by the increasing adoption of precision agriculture technologies and the urgent need for efficient weed management in pasture lands. By 2033, the global market is forecasted to attain a value of USD 5.94 billion, reflecting the expanding role of drones in modern agricultural practices and the integration of advanced imaging and AI technologies for weed identification.

The surge in the drone-assisted pasture weed identification market is propelled by several key growth factors. Firstly, the rising global demand for sustainable agricultural practices is encouraging the adoption of drone technology for weed control. Traditional weed management methods are often labor-intensive, time-consuming, and sometimes environmentally damaging due to excessive herbicide use. Drone-assisted weed identification offers a precise, data-driven approach, enabling targeted herbicide application, reducing chemical usage, and minimizing environmental impact. Furthermore, the integration of artificial intelligence and multispectral imaging with drones has significantly improved weed detection accuracy, making the technology increasingly attractive to both large-scale agricultural enterprises and smallholder farmers. As a result, the market is witnessing rapid penetration in regions with advanced agricultural infrastructure and a strong emphasis on sustainability.

Secondly, the growing labor shortages in the agricultural sector are further fueling the demand for drone-assisted solutions. Many countries are experiencing a decline in the availability of skilled farm labor, making it challenging to carry out manual weed identification and control. Drones equipped with advanced sensors and AI-based analysis can cover vast pasture areas quickly and efficiently, providing real-time data to farmers and enabling timely intervention. This efficiency not only addresses labor constraints but also enhances overall productivity and profitability for farmers. Additionally, government initiatives and subsidies aimed at promoting precision agriculture and digital farming are providing further impetus to the market, particularly in developed economies where regulatory frameworks are more supportive of drone operations.

Another significant growth driver is the increasing awareness among livestock farmers and agricultural enterprises regarding the economic losses caused by invasive weed species in pasture lands. Weeds compete with forage crops for nutrients, water, and sunlight, leading to reduced pasture productivity and lower livestock yields. Drone-assisted weed identification enables early detection and precise mapping of weed infestations, allowing for targeted control measures and improved pasture management. The ability to monitor weed populations over time and assess the effectiveness of control strategies is also contributing to the widespread adoption of this technology. As the benefits of drone-assisted weed identification become more evident, the market is expected to experience continued expansion across various end-user segments.

From a regional perspective, North America currently dominates the drone-assisted pasture weed identification market, accounting for the largest share in 2024. This leadership can be attributed to the region's advanced agricultural sector, high adoption rates of precision farming technologies, and supportive regulatory environment. Europe follows closely, driven by stringent environmental regulations and a strong focus on sustainable agriculture. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, owing to increasing investments in agricultural modernization and rising awareness about the benefits of drone technology. Latin America and the Middle East & Africa are also emerging as promising markets, supported by government initiatives and the expansion of commercial agriculture.

Drone Type Analysis

The drone type segment of the drone-assisted pasture weed identification market is categorized into fixed-wing, rotary-wing, and hybrid drones. Fixed-wing drones are favored for their ability to cover large areas efficiently, making them ide

Images contains bean, with various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) The ground truth is defined for each images with polygons around leafs boundaries: In addition, each polygons are labeled into crop or weed. Due to the nature of the camera, a spectral band registration is required and performed with a registration method based on previous work (with a sub-pixel registration accuracy). The alignment is refined in two steps, with (i) a rough estimation of the affine correction and (ii) a perspective correction for the refinement and accuracy through the detection and matching of key points. The result shows that GFTT algorithm is the best key-point detector considering the 570 nm band as spectral reference for the registration. After the registration, all spectral images are cropped to 1200* 800 px and concatenated to channel-wise.

Drone-Assisted Pasture Weed Identification Market Outlook

According to our latest research, the global Drone-Assisted Pasture Weed Identification market size reached USD 412.7 million in 2024, with a robust CAGR of 16.8% expected throughout the forecast period. By 2033, the market is projected to achieve a value of USD 1,460.3 million, driven by the rapid adoption of precision agriculture technologies and the increasing need for efficient pasture management. The primary growth factor stems from the agricultural sector’s urgent demand for innovative weed management solutions that minimize manual labor, optimize herbicide usage, and enhance overall pasture productivity.

One of the core drivers propelling the Drone-Assisted Pasture Weed Identification market is the growing emphasis on sustainable agriculture and environmental stewardship. As regulatory pressures mount globally to reduce chemical inputs and mitigate environmental impact, farmers and land managers are turning to drone-based technologies for precise weed detection and targeted herbicide application. Drones equipped with advanced imaging technologies enable accurate mapping and density assessment of invasive weed species, facilitating data-driven decision-making. This shift not only supports the adoption of integrated weed management practices but also helps farmers minimize input costs, reduce environmental contamination, and improve pasture health, thereby fueling market growth.

Another significant growth factor is the increasing labor shortages and rising operational costs in the agricultural sector. Traditional methods of weed identification and management are labor-intensive, time-consuming, and often inefficient for large-scale pasturelands. The integration of drones into pasture management workflows addresses these challenges by automating weed scouting, mapping, and monitoring processes. With advancements in drone hardware, imaging sensors, and data analytics, stakeholders can now achieve higher accuracy and efficiency, reducing the reliance on manual labor and enabling timely interventions. This technological evolution is especially critical in regions facing acute labor shortages and escalating wage rates, further accelerating the adoption of drone-assisted solutions.

Additionally, the expanding availability and affordability of sophisticated imaging technologies such as multispectral, thermal, and LiDAR sensors are catalyzing market expansion. These technologies enable drones to capture high-resolution data that can distinguish between various weed species, assess weed density, and monitor pasture health in real-time. The integration of artificial intelligence and machine learning algorithms enhances weed identification accuracy, enabling proactive and site-specific management strategies. As technology costs continue to decline and regulatory frameworks become more supportive, the accessibility of drone-assisted weed identification solutions is expected to increase, driving widespread adoption across both developed and emerging markets.

From a regional perspective, North America currently dominates the Drone-Assisted Pasture Weed Identification market, accounting for the largest market share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technologically advanced agricultural practices, high awareness among farmers, and supportive government initiatives in these regions have been instrumental in market growth. Meanwhile, Asia Pacific is anticipated to exhibit the fastest growth rate during the forecast period, fueled by the rapid modernization of agriculture, increasing investments in precision farming, and rising demand for sustainable pasture management solutions. Latin America and the Middle East & Africa are also expected to witness significant growth as drone technology becomes more accessible and local governments prioritize agricultural innovation.

Drone Type Analysis

The drone type segment in the Drone-Assisted Pasture Weed Identification market is

Weeds are the major limitation to efficient crop production, and effective weed management is necessary to prevent yield losses due to crop-weed competition. Assessments of the relative efficacy of weed control treatments by traditional counting methods is labor intensive and expensive. More efficient methods are needed for weed control assessments. There is extensive literature on advanced techniques of image analysis for weed recognition, identification, classification, and leaf area, but there is limited information on statistical methods for hypothesis testing when data are obtained by image analysis (RGB decimal code). A traditional multiple comparison test, such as the Dunnett-Tukey-Kramer (DTK) test, is not an optimal statistical strategy for the image analysis because it does not fully utilize information contained in RGB decimal code. In this article, a bootstrap method and a Poisson model are considered to incorporate RGB decimal codes and pixels for comparing multiple treatments on weed control. These statistical methods can also estimate interpretable parameters such as the relative proportion of weed coverage and weed densities. The simulation studies showed that the bootstrap method and the Poisson model are more powerful than the DTK test for a fixed significance level. Using these statistical methods, three soil disinfestation treatments, steam, allyl-isothiocyanate (AITC), and control, were compared. Steam was found to be significantly more effective than AITC, a difference which could not be detected by the DTK test. Our study demonstrates that an appropriate statistical method can leverage statistical power even with a simple RGB index.

Pearson’s correlation coefficients (N = 63) between yield and weed parameters.