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市場調查報告書
商品編碼
1494858
到 2030 年農業人工智慧市場預測:按作物類型、成分、部署模式、技術、應用、最終用戶和地區進行的全球分析Artificial Intelligence in Agriculture Market Forecasts to 2030 - Global Analysis By Crop Type, Component, Deployment Mode, Technology, Application, End User and By Geography |
根據 Stratistics MRC 的數據,2024 年全球農業人工智慧市場規模為 19.5 億美元,預計預測期內年複合成長率為 25.2%,到 2030 年將達到 65.3 億美元。
農業中的人工智慧是指應用機器學習、電腦視覺、機器人技術和資料分析來增強農業運作。人工智慧主導的技術透過分析各種資訊來源的資料(包括土壤感測器、天氣預報和衛星圖像)來實現精密農業。這些技術有助於最佳化作物產量、減少資源使用並最大限度地減少對環境的影響。人工智慧簡化了害蟲檢測、作物監測和自動收割等任務,使農業經營更有效率、更永續性、更盈利。
據 NASSCOM 稱,到 2025 年,資料和人工智慧技術將為印度農業部門付加約 900 億美元的價值。總體而言,到 2025 年,人工智慧預計將為印度 GDP 增加約 5,000 億美元。
糧食生產需求增加
糧食生產的需求增加將推動農業人工智慧的發展,因為需要高效的資源利用、最大化產量和永續的實踐。精密農業、預測分析和自動化機械等人工智慧技術將最佳化資源利用、提高作物產量並減少廢棄物。隨著世界人口的成長,農民將採用人工智慧來永續滿足糧食供應需求。先進的人工智慧應用將透過促進即時監測、害蟲管理和資料主導的決策來推動市場成長,使農業更具彈性和應對挑戰的能力。
缺乏技術專長
農業人工智慧(AI)技術專業知識的缺乏是由於該行業對傳統耕作方法的依賴以及對先進技術的接觸有限。技術知識不足正在阻礙人工智慧的全部潛力得到充分利用,阻礙創新、資料主導的決策和農業整體生產力的提高。因此,人工智慧技術的採用速度將會放緩,限制其市場擴張和對該產業的變革性影響。
加大對農業技術新興企業的投資
增加對農業技術新興企業的投資將推動先進的人工智慧驅動解決方案的創新和開發。這些投資將使新興企業能夠透過機器學習、電腦視覺和資料分析等人工智慧技術加強精密農業、最佳化資源利用並提高作物產量。增加的資金籌措將加速研究和開發,以創建更強大和可擴展的人工智慧應用程式,從而改變農業實踐、提高生產力並應對氣候變遷和糧食安全等挑戰。
初期投資成本高
農業人工智慧需要先進的技術、基礎設施和熟練的人力資源,導致初始投資成本高。開發和實施機器學習演算法、機器人和物聯網設備等人工智慧系統需要大量資金。因此,市場成長受到廣泛採用放緩、進入障礙以及農業部門技術進步和生產力成長整體步伐放緩的阻礙。
COVID-19 的影響
COVID-19 大流行凸顯了食品供應鏈對自動化和彈性的需求,並加速了人工智慧在農業中的採用。勞動力短缺和物流中斷引發了人們對人工智慧主導的精密農業、遠端監控和自動收割解決方案的興趣。然而,經濟不確定性和供應鏈中斷也帶來了挑戰,影響了農業人工智慧技術的投資和實施時間表。
機器人與自動化產業預計將在預測期內成為最大的產業
機器人和自動化領域預計將出現良好的成長。農業機器人和自動化利用人工智慧來提高效率和生產力。自動曳引機、無人機和機器人收割機使用人工智慧來執行種植、澆水和收割等精準任務。這些技術可以即時監測和管理作物,降低人事費用並提高產量。人工智慧主導的自動化可確保資源的最佳利用,最大限度地減少浪費,並有助於資料主導的決策,以實現更好的作物管理和永續性。
預計現場準備部分在預測期間內年複合成長率最高
預計在預測期內,田間準備產業將以最高的年複合成長率成長。人工智慧主導的農業中的田間準備涉及使用土壤感測器、無人機和機器學習演算法等技術來分析土壤健康、濕度水平和養分含量。這些資料指南農民最佳化犁地、種植計劃和土壤處理,從而提高作物產量、降低投入成本和永續的農業實踐。人工智慧支援精確的田間測繪和決策,提高農業的整體效率和生產力。
由於糧食需求增加、政府措施和技術進步,預計亞太地區在預測期內將佔據最大的市場佔有率。中國、印度和日本等國家在將人工智慧應用於精密農業、作物監測和自動化機械方面處於領先地位。快速的都市化、技術進步和不斷變化的飲食偏好正在重塑市場動態。該地區龐大的農業基地,加上對農業科技新興企業投資的增加,正在推動人工智慧解決方案的創新和實施。
在該地區精密農業技術的推動下,預計歐洲在預測期內將出現最高的年複合成長率。歐洲既有小型家庭農場,也有大型商業農場,人們越來越關注永續性和有機生產方法。歐洲的支持性法規環境和政府措施極大地促進了數位農業。這一趨勢表明,人工智慧在歐洲農業中的整合前景廣闊,並將徹底改變該行業的營運格局。
According to Stratistics MRC, the Global Artificial Intelligence in Agriculture Market is accounted for $1.95 billion in 2024 and is expected to reach $6.53 billion by 2030 growing at a CAGR of 25.2% during the forecast period. Artificial Intelligence in agriculture refers to the application of machine learning, computer vision, robotics, and data analytics to enhance farming practices. AI-driven technologies enable precision farming by analyzing data from various sources such as soil sensors, weather forecasts, and satellite imagery. These technologies assist in optimizing crop yields, reducing resource usage, and minimizing environmental impact. Tasks such as pest detection, crop monitoring, and automated harvesting are streamlined through AI, leading to improved efficiency, sustainability, and profitability in agricultural operations.
According to NASSCOM, by 2025, approximately USD 90 billion of value will be added to the agriculture sector through data and AI technologies in India. With all the sectors combined, artificial intelligence is projected to add approximately USD 500 billion to India's GDP by 2025.
Increasing demand for food production
Increasing food production demand drives AI growth in agriculture by necessitating efficient resource use, yield maximization, and sustainable practices. AI technologies, such as precision farming, predictive analytics, and automated machinery, optimize resource use, improve crop yields, and reduce waste. As the global population rises, farmers adopt AI to meet food supply demands sustainably. Advanced AI applications facilitate real-time monitoring, pest and disease management, and data-driven decision-making, making agriculture more resilient and responsive to challenges, thereby propelling market growth.
Lack of technical expertise
The lack of technical expertise in Artificial Intelligence (AI) in agriculture stems from the sector's traditional reliance on conventional farming methods and limited exposure to advanced technologies. Insufficient technical know-how leads to underutilization of AI's potential, hindering innovation, data-driven decision-making and overall productivity improvements in agriculture. Consequently, the adoption rate of AI technologies slows, limiting the market's expansion and its transformative impact on the sector.
Rising investments in agritech start-ups
Rising investments in agritech start-ups fosters innovation and development of advanced AI-driven solutions. These investments enable start-ups to enhance precision farming, optimize resource utilization, and improve crop yield through AI technologies like machine learning, computer vision, and data analytics. Increased funding accelerates research and development, leading to more robust and scalable AI applications, thereby transforming agricultural practices, boosting productivity, and addressing challenges such as climate change and food security.
High initial investment costs
Artificial Intelligence in agriculture involves high initial investment costs due to the need for advanced technologies, infrastructure, and skilled personnel. Developing and implementing AI systems, such as machine learning algorithms, robotics, and IoT devices, requires substantial financial resources. Consequently, market growth is hampered as widespread implementation is slowed, creating a barrier to entry and reducing the overall pace of technological advancement and productivity improvements in the agricultural sector.
Covid-19 Impact
The covid-19 pandemic accelerated the adoption of AI in agriculture by highlighting the need for automation and resilience in food supply chains. Labor shortages and disrupted logistics spurred interest in AI-driven solutions for precision farming, remote monitoring, and automated harvesting. However, economic uncertainties and disrupted supply chains also posed challenges, affecting investment and implementation timelines for AI technologies in the agricultural sector.
The robotics & automation segment is expected to be the largest during the forecast period
The robotics & automation segment is estimated to have a lucrative growth. Robotics and automation in agriculture leverage AI to enhance efficiency and productivity. Autonomous tractors, drones, and robotic harvesters use AI for precision tasks like planting, watering, and harvesting. These technologies enable real-time monitoring and management of crops, reducing labor costs and increasing yields. AI-driven automation ensures optimal use of resources, minimizes waste, and helps in making data-driven decisions for better crop management and sustainability.
The field preparation segment is expected to have the highest CAGR during the forecast period
The field preparation segment is anticipated to witness the highest CAGR growth during the forecast period. Field preparation in AI-driven agriculture involves using technologies like soil sensors, drones, and machine learning algorithms to analyze soil health, moisture levels, and nutrient content. This data guides farmers in optimizing tillage, planting schedules, and soil treatment, leading to improved crop yields, reduced input costs, and sustainable farming practices. AI aids in precise field mapping and decision-making, enhancing overall efficiency and productivity in agriculture.
Asia Pacific is projected to hold the largest market share during the forecast period due to increasing food demand, government initiatives, and advancements in technology. Countries like China, India, and Japan are leading in adopting AI for precision farming, crop monitoring, and automated machinery. Rapid urbanization, technological advancements, and shifting dietary preferences are reshaping the market dynamics. The region's large agricultural base, coupled with rising investments in AgriTech start-ups, fosters innovation and implementation of AI solutions.
Europe is projected to have the highest CAGR over the forecast period, driven by the region's precision farming techniques. Europe is marked by a mix of small-scale family farms and large commercial operations, with an increasing focus on sustainability and organic production methods. Europe's supportive regulatory environment and government initiatives are highly promoting digital agriculture. This trend indicates a promising future for AI integration in European agriculture, poised to revolutionize the sector's operational landscape.
Key players in the market
Some of the key players profiled in the Artificial Intelligence in Agriculture Market include IBM Corporation, Microsoft Corporation, Deere & Company, Bayer AG, Trimble Inc., AG Leader Technology, Cropin Technology Solutions Pvt. Ltd., Agribotix LLC, Prospera Technologies, Descartes Labs, Taranis, Corteva, aWhere Inc., Ceres Imaging and Gamaya.
In April 2024, Cropin launched Aksara, a generative AI system for climate smart agriculture. Aksara will cover nine crops such as paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millets for 5 countries in the Indian subcontinent. This generative AI system can suggest farmers which inputs to use for crops like rice or maize under specific agro-climatic conditions or provide climate smart agri-advisories, the company said in a statement.
In June 2023, Deere & Company has unveiled its first fully autonomous tractor, which is already operational on select farms and available for purchase. This tractor is a product of 20 years of AI development and is designed to complete tasks on time, every time, and at a high level of quality.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.