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Synopsis
The global market for Imaging Technology for Precision Agriculture was estimated to be worth US$ 94.1 million in 2024 and is forecast to a readjusted size of US$ 210 million by 2031 with a CAGR of 12.3% during the forecast period 2025-2031.
Precision agricultural imaging technology refers to the use of advanced imaging equipment and technology to collect and analyze high-resolution, multi-band images of farmland, crops and vegetation to achieve precise management and monitoring of agricultural production. These imaging technologies mainly include multispectral and hyperspectral imaging, which provide comprehensive and quantitative information for agricultural production, helping farmers and agricultural professionals make scientific decisions and improve crop yields, quality and sustainability.
Precision agricultural imaging technology is an advanced technology that optimizes agricultural production by using high-tech imaging methods. It combines multidisciplinary technologies such as remote sensing, image processing, and artificial intelligence to provide data support and decision-making basis for agricultural production. In recent years, with the acceleration of agricultural modernization, precision agricultural imaging technology has gradually become an important tool in the agricultural field and is widely used in soil testing, crop growth monitoring, pest and disease prediction, climate change analysis, etc. Through satellite remote sensing, drones, unmanned vehicles and other equipment, agricultural producers can obtain more accurate agricultural data, thereby achieving precise fertilization, precision irrigation, precision spraying and other operations, greatly improving resource utilization efficiency and agricultural output.
At present, the precision agricultural imaging technology market is showing a booming trend. The scale of the global precision agriculture market is growing rapidly and is expected to continue to expand in the next few years.
Especially in developed countries and regions, agricultural producers are increasingly relying on scientific and technological means to improve production efficiency and product quality. In these areas, the application of drone imaging technology is relatively common, helping agricultural workers to accurately monitor crop health, soil conditions, etc., and then optimize production management plans. In addition, the combination of artificial intelligence and big data technologies has made farm management more intelligent. Data analysis and predictive models can help agricultural decision makers better understand soil fertility, crop disease development, etc., thereby reducing the use of pesticides and chemical fertilizers and reducing environmental pollution.
Global key players of Imaging Technology for Precision Agriculture include Corning (NovaSol), IMEC, Headwall Photonics, Specim, Teledyne Dalsa, etc. The top five players hold a share about 41%. North America is the largest market, and has a share about 47%, followed by Europe and China with share 21% and 15%, separately. In terms of product type, Multispectral technology is the largest segment, occupied for a share of 43%. In terms of application, Farm has a share about 79 percent.
This report aims to provide a comprehensive presentation of the global market for Imaging Technology for Precision Agriculture, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Imaging Technology for Precision Agriculture by region & country, by Type, and by Application.
The Imaging Technology for Precision Agriculture market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Imaging Technology for Precision Agriculture.
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Published By : QY Research