Global overview
Fast-growing patent activity mainly focusing on North America
Analysis of data from 1,529 international patent families in the field of Predictive models in precision agriculture shows a substantial CAGR of +27.1% from 2017 to 2021, indicating a surge in interest in the subject matter (Figure 2.22).
The USPTO, WIPO and EPO are widely recognized as the leading global authorities for patent filings, with 1,138, 1,055, and 823 international patent families respectively (Figure 6.1). Asia is also rapidly emerging as a significant contributor in the field of Predictive models in precision agriculture. China leads the pack with 635 international patents, followed closely by Japan with 391 international patents and the Republic of Korea with 284 international patents. This trend highlights the region's increasing presence and influence in the global market.
On the other hand, North America sees Canada contributing 459 international patent families, while Latin America and the Caribbean are represented by Brazil with 472 international patent families. Oceania is also making its mark, with Australia filing 378 international patent families. It is important to note that this data only considers international patent families, which may not fully capture the impact of regional jurisdictions in Asia.
Inventive regions
North America as the innovation hub for Predictive models in precision agriculture
According to Figure 6.2, the United States is the leader in R&D for predictive models dedicated to the Agrifood sector, with a total of 809 international patent families. Following close behind is China with 136 international patent families, and Japan with 106 international patent families. All the regions have shown significant growth in this area, with a CAGR between 2017 and 2021, exceeding 20% for several countries. Specifically, EPO has seen a growth rate of 58.5%, Japan has experienced a growth rate of 53.4%, the Republic of Korea has seen a growth rate of 49.6%, India has grown at a rate of 35.1%, Germany has seen a growth rate of 29.7%, and China has grown at a rate of 27.9%.
North America
The United States boosts investments in science, evidence, and programs for effective conservation and climate-smart practices: The FDA is using the One Health Approach to address issues associated with food and public health, USDA is surveilling emerging zoonotic diseases to mitigate future pandemic risk, the National Oceanic and Atmospheric Administration (NOAA) is increasing data collection to model and predict the impact of climate change on fisheries production, and the National Aeronautics and Space Administration (NASA) is collaborating with USDA to share and apply space-based measurements of soil moisture to strengthen predictions of agricultural and climate trends and support research on the carbon cycle.
Europe
To assist partner jurisdictions to strengthen their food systems, the European Union is mobilizing various financial instruments, including the Neighborhood, Development and International Cooperation Instrument – Global Europe (NDICI-GE). Sustainable agriculture is part of the Global Gateway strategy, Europe’s positive offer to the world to promote sustainable investments in view of the twin green and digital transition and human development – in cooperation with the European Union’s Member States, financial institutions, development finance institutions and private sector.
The United Kingdom government is pioneering the use of new tools to support its regulatory modernization program, including the use of open data and machine learning to strengthen its strategic surveillance capabilities. This system is used to predict internal and external risks to the United Kingdom’s food system before they happen, complementing the effectiveness of import controls and incident management systems and helping to further protect consumers.
Asia
In Asia, China is willing to deepen international Agrifood scientific and technological cooperation in "an open, inclusive, mutually beneficial and win-win manner; promote pragmatic cooperation in digital agriculture, green agriculture, smart food, green grain storage, agricultural machinery and equipment, molecular breeding and quality and safety; consolidate competitive resources of all parties on the basis of mutual trust; make full use of bilateral and multilateral agriculture and food security cooperation mechanisms; deepen South–South and triangular cooperation; strengthen North–South dialog; pursue innovative formats of experience sharing and talent exchange and cooperation; jointly improve global agricultural science and technology".
There has been an immense growth of digital technologies in the agricultural sector in India in the last five years. Digital tools or apps based on AI for providing better farm management practices are gaining currency in India.
Viet Nam is strengthening the application of digital technology in controlling precise agricultural farming systems and monitoring carbon footprints for key food value chains. Viet Nam is also involved in the building of digital information platforms for weather, climate, warning of risks caused by natural hazards, climate change, forecasting and warning of diseases and pests, and market information with easy access at provincial/commune levels. Finally Viet Nam is promoting the application of planting area/farm codes for all crops and livestock, application of digital technology in planting area management and traceability.
In Bhutan, an ensemble of digital tools is planned to be developed and rolled out to provide crop and livestock advisory services, early warning on weather and incidences of pests and diseases. To assist service delivery, such digital platforms will also be designed to support agriculture and livestock extension agents. Digital tools to collect real-time data on farm conditions will also be rolled out to track the pulse and health of Bhutan’s Agrifood systems.
Indonesia, Nepal and Mongolia also support farmers’ capacity, building on technology and innovation, including digital transformation. Tajikistan disseminates knowledge and encourages farmers to apply “smart” farm technologies and practices to increase yield and protect the environment. Among others, it promotes the rotation of cultivated plants, pasture management, animal, poultry and bee breeding and feeding, manure and agrichemicals management, and the use of digital technology in raising animals.
Kuwait is investing in AI, advanced technologies and digitization to keep up with the progress in food systems.
Türkiye is integrating new technological advancements, i.e. smart systems and AI, in national food production platforms to minimize human error in food production.
Africa
In Africa, Namibia aims to transform institutions for R&D and capacity building and to generate updated and open data. Emphasis is given to regularly conduct nutrition surveys to generate timely data on issues pertaining to wellness and survival, as the last survey was conducted in 2013. Zimbabwe aims at attracting youths to agriculture, “making agriculture sexy” through digitalization and other smart technologies that reduce that reduce drudgery.
Latin America and the Caribbean
In Latin America, Uruguay is promoting the complete digitalization of agriculture, with special emphasis on the use of information and computing technologies by medium and family producers, as well as Micro, Small and Medium Enterprises (MSMEs) that process and market food.
Oceania
In Oceania, Fiji promotes the use of block chain technology and digitalization to help to engage younger people in the food system, as for example in the use of drones for monitoring land-use changes or digital devices to measure changes in the ocean temperature. Fiji will also provide accessible digital platforms that can provide information and guidance to producers/end-users for the design, planning and implementation of their activities and to enable e-trade.
Top players
Mixed panorama between AgriTech pure players and IT corporate heavyweights
The predictive model segment is largely dominated by industrial actors, with 90% of the market share. The top players in this segment originate from the United States, Germany and Japan, with global machine manufacturers and agrochemical companies leading the way (Figure 6.3).
As a global main actor for AgriTech in general, Deere is also the main innovator of predictive computational models as well as their integration within automated machine controls, holding 116 international patent families. Deere holds patents covering predictive crop state and characteristic mapping, predictive nutrient mapping, predictive harvesting models for machine control, predictive yield mapping and more. Deere exhibits a strong and diversified portfolio. However, this portfolio only shows extensions mainly in Brazil and Europe to a lesser extent.
Bayer and BASF are two German chemical and agrochemical companies, holding 72 and 53 international patent families respectively in the field of Predictive models in precision agriculture, ranking second and third. Bayer developed machine learning-based systems for soil analysis, multivariate algorithms for yield prediction in horticulture, image processing for disease detection in plants, fertility prescription and more. BASF shows a large expertise in crop protection and pest control based on infestation predictions, weather image capture complemented by analysis algorithms and prediction models for formulation properties. Both portfolios are strong and diversified, with worldwide extensions.
Mineral Earth Sciences and Alphabet are both US companies specializing in cutting-edge technology. They hold 21 and 12 international patent families respectively in this field, ranking fourth and sixth. Mineral Earth Science offers a wide range of services including soil composition prediction, crop yield prediction, climate forecasting and plant imaging analysis. In contrast, Alphabet has developed a comprehensive suite of solutions tailored specifically for aquaculture and fish feeding industries.
TBG AG (Thyssen-Bornemisza Group) is a Swiss company which acquired DTN (former Telvent and Schneider Electric) in 2017. TBG AG currently holds 10 international patent families in this field, ranking eighth. Together with Iteris, DTN developed models for soil compaction from diagnosis and prediction of soil and weather conditions to ultimately improve ground performance.
IBM (United States) shows a restricted portfolio related to the AgriTech domain from the development of IoT agricultural ecosystems, machine learning models for livestock value chain, crop disorder detection and systems for crop identification using satellite. It currently holds nine international patent families related to Predictive models in precision agriculture, ranking ninth.
TATA Sons PVT is an Indian actor involved in system development for crop features and quality management. Tata’s portfolio is mostly extended to the United States, Brazil and Europe. It currently holds seven international patent families related to Predictive models in precision agriculture, ranking fourteenth.
Zheijiang University and Climate Foundation are the two academic players of this list. Zheijiang University currently holds 12 international patent families related to Predictive models in precision agriculture, ranking fifth. This reflects the university's extensive expertise in yield prediction for crops and seeds, as well as in nitrogen measurement in soil content. Climate Foundation shows innovation in machine learning strategies for geospatial and weather data processing. It holds seven international patent families related to this technology, ranking thirteenth.
Emerging technologies: data collection, processing, and controlling
Technologies for data collection, processing, and controlling systems are driving the adoption of Predictive models in precision agriculture
By analyzing the IPC subclasses within the field of Predictive models in precision agriculture, statistics were compiled on the number of international patent families associated with each IPC subclass, along with the CAGR of these subclasses from 2017 to 2021. Figure 6.4 illustrates the most-utilized technologies in the area of predictive models and their applications in agriculture.
Through the analysis of international patent families related to Predictive models in precision agriculture, the technologies involved in predictive models can be categorized into three main areas: data collection, data processing and controlling systems.
Data collection forms the foundation of Predictive models in precision agriculture. By utilizing sensor data, accelerometers, captured images, cameras and communication interfaces, a variety of real-time information about the farmland can be obtained. This data is used to monitor crop growth conditions, soil moisture, temperature and other environmental parameters, thereby providing the basis for subsequent data processing and decision-making. The relevant IPC classifications for this technology include: G06T, G06V, G01N, G01S, G01C and H04N.
In the data processing stage, predictive models analyze the collected data through machine learning, data classification and correlation techniques. These models can identify patterns and trends within the data, assess crop health, predict pest infestations and optimize the growth environment. The key to this stage is transforming complex raw data into valuable information that helps farmers and agricultural enterprises make informed decisions. The IPC classifications relevant to data processing include G06Q, G06N, G06F and G06K.
Controlling systems are employed in precision agriculture to manage and regulate various agricultural processes. These systems can control non-electric variables and other critical parameters to optimize the operation of agricultural equipment and processes. For example, controlling systems can automatically adjust irrigation systems, fertilization equipment and pesticide sprayers, thereby enhancing the efficiency and effectiveness of agricultural production. The IPC classifications pertaining to controlling systems include G05D and G05B.
By integrating advanced predictive model technologies at every stage – data collection, data processing and controlling systems — agricultural enterprises can streamline operations, optimize decision-making processes and ultimately achieve better production outcomes. The application of these technologies not only enhances the precision and efficiency of agriculture but also promotes the shift towards intelligent and data-driven agricultural practices.
In the field of Predictive models in precision agriculture, the majority of patent applications are concentrated on data processing technologies, followed by data collection.
Applications of predictive models within the precision agriculture sub-domain was achieved by counting international patent families in-related IPC subclasses (Figure 6.5).
In recent years, there has been a significant increase in the use of predictive models to enhance soil management, plant culture and animal husbandry practices worldwide. This growth is evidenced by a CAGR of over 20% from 2017 to 2021. Innovations in the application of predictive models have revolutionized various sectors including soil working, horticulture, harvesting, planting, animal husbandry, aviculture, apiculture, pisciculture, fishing and animal trapping.
The integration of predictive models in soil management, includes soil working (A01B), harvesting and mowing (A01D) as well as planting, sowing, fertilizing (A01C). This has led to more efficient and sustainable agricultural practices. By analyzing data on soil composition, moisture levels and nutrient content, farmers can make informed decisions on irrigation, fertilization and crop rotation. This not only improves crop yields but also helps preserve soil health for future generations.
In horticulture (A01G), predictive models are being used to optimize plant growth and development. By monitoring factors such as temperature, humidity and sunlight exposure, growers can create ideal conditions for plant growth and minimize the risk of pests and diseases. This has led to higher-quality produce and increased profitability for farmers.
The use of predictive models in animal husbandry has also transformed the way livestock is managed. Animal husbandry, aviculture, apiculture, pisciculture and fishing have A01K as an IPC subclass, and catching, trapping or scaring of animals is A01M. By analyzing data on animal behavior, health and nutrition, farmers can identify potential issues before they escalate and take proactive measures to ensure the well-being of their animals. This has not only improved animal welfare but also increased productivity and profitability for farmers.
However, if predictive models are found in major areas of AgriTech segments, it fails to deeply penetrate and support FoodTech applications, for now (including food supply chain, food services, food delivery).
Publication number: EP3482630
Applicant: Efos d.o.o.
Application Date: 13.11.2017
This invention presents a system and method for automated pest forecasting using data from traps, weather sources, and user inputs. By employing visual processing, machine learning, and decision-making algorithms, the system predicts pest populations and provides recommendations, improving crop protection efficiency.
The patent describes a novel approach to peat forecasting by integrating data from traps, weather sources, and user inputs. The system’s automated processes improve the accuracy and timeliness of pest predictions, leading to more effective crop protection strategies.
The system offers advantages over manual pest monitoring methos by enabling real-time pest forecasts, reducing reaction time to pest pressure, and optimizing crop protection measures. It minimizes the risk of pest resistance, enhances prediction accuracy, and reduces operational costs.