Precision Agriculture in Crop Production
Key Technologies of Precision Agriculture
Precision agriculture technologies are playing an increasing role in farm production. Precision agriculture technologies can improve resource management through the precise application of inputs, such as water, fertilizer, and feed, leading to more efficient agricultural production. Precision agriculture can be implemented through a suite of technologies that can be used in isolation or in conjunction with other technologies.
Internet of Things
Agricultural Internet of Things (IoT) refers to connecting devices and tools, such as sensors, to the internet, allowing them to transfer data. The IoT is a vast network with many objects connected to a global information infrastructure. In agriculture, IoT technology has the potential to revolutionize the way farms operate and increase the efficiency and productivity of the industry.
Big Data
In agriculture, “big data” refers to the massive amount of data generated by agricultural activity and measurement. Processing and managing large amounts of data is complicated when using standard approaches and systems. Furthermore, big data empowers agricultural practitioners to gain information about different factors that impede agricultural production and make efficient decisions in daily farming. Big data analytics in agriculture entails the processing and analysis of substantial amounts of data to get important insights. Big data analytics can anticipate agricultural harvesting time, soil quality, crop protection, and irrigation requirements.
Cloud Computing
Cloud computing technology is mainly used for information processing, and it can effectively solve the problem of storage, calculation, and the processing of massive agricultural production data. Many emerging cloud service platforms can realize the storage, searching, and analysis of massive agricultural information. Cloud computing technology includes data mining, data analysis, artificial intelligence (AI), and other technologies.
Data Fusions
Data fusion approaches combine data from several sources, such as sensors, satellites, drones, and weather stations. By integrating these diverse data sources, a thorough comprehension of field conditions is achieved, which improves the precision and dependability of agricultural decision-making procedures. In order to enhance agricultural decision-making processes, it is essential to integrate diverse data sources and utilize big data analytics in agriculture. By processing substantial amounts of data, valuable insights can be gained, patterns can be recognized, trends can be forecasted, and farming methods can be improved for increased efficiency and sustainability.
Artificial Intelligence
Technology in agriculture has increased in complexity in recent years. With the growing use of precision farming, data-driven decision-making, and advanced analytics using machine learning algorithms and other artificial intelligence (AI) technologies, farmers can now collect and analyze vast amounts of data about their crops, including weather patterns, soil health, and plant growth. With AI to optimize farming practices, farmers can improve their crop yields, reduce waste, and increase profitability. Accurate and timely data can make the difference between a successful or unsuccessful crop cycle, making it crucial for farmers to leverage technology and data to their advantage.
Wireless Sensor Networks
Effective agricultural production requires comprehensive monitoring of plant information, which is crucial for enhancing production, increasing profitability, and ensuring high-quality product development. Various environmental factors, including temperature, humidity, air pressure, carbon dioxide levels, soil temperature, and soil pH, play a crucial role in crop growth. IoT devices integrated into agricultural systems can sense and analyze these environmental factors, enabling remote field monitoring and the creation of an optimal farming environment tailored to these variables.
Global Navigation Satellite System
A global navigation satellite system (GNSS) is a global positioning system with three-dimensional positioning and navigation functions. It has the characteristics of being all-weather, omni-directional, and high-precision. GNSS technology can correct and supplement the temporal influence of remote sensing technology and accurately grasp the dynamic location information of a certain region. The application of GNSS technology in precision seeding and fertilization, agricultural machinery, and pest control can provide farmers with effective help and improve operational efficiency and agricultural output. In the joint operation of agricultural machinery, the output sensor and GNSS technology can be combined to obtain the distribution data of the output of each crop in the farmland.
Remote Sensing
Remote sensing (RS) technology plays an important role in managing and protecting farmland water conservancy projects, monitoring the ecological environment, and making real-time decisions about agricultural fertilization. RS technology uses different forms of sensors to receive various electromagnetic wave information of ground objects from RS platforms at different heights. Remote sensing can indicate variations in the colors of the field that correspond to changes in soil type, crop development, field boundaries, roads, water, etc.
Sensors
The information in agricultural IoT is mainly obtained through many sensors. In agriculture, sensors are mainly used for environmental information monitoring, animal and plant life perception, and quality, safety, and traceability. Today’s smart sensors are fundamental to data-driven decision-making in PA, facilitating farmers’ monitoring and optimization of various parameters vital to crop health and resource management.
Geographical Information Systems
Geographic information systems (GIS) in farming and agriculture industries are specifically designed to capture, manipulate, analyze, and present spatial or geographic data related to agriculture. GIS is critical in making informed decisions regarding crop scheduling, pest management, irrigation, and crop yield estimation, among other uses. The integration of GIS in agriculture provides a systematic approach to studying and managing agricultural resources. Agricultural GIS is a technology-driven practice that combines spatial data (about land use, topography, climate, soils, etc.) and temporal data (seasons, crop cycles, weather patterns, etc.) to help farmers and agriculturists manage their lands more effectively.
Variable Rate Application
Variable rate application (VRA) refers to the application of different types and quantities of agricultural inputs according to the specific needs of a given area. This is achieved using GPS and GIS technology to map the field, sensors to measure variation in the field, and equipment capable of changing the rate of application on the fly. This contrasts with a traditional “blanket” approach, where the same amount of inputs is applied across the entire field. Complementing VRA is variable rate technology (VRT) that helps achieve the fundamental objective of precision farming by enabling the optimum application of water, nutrients, chemicals, etc., based on varying site-specific needs.
Auto-Guidance Systems
Auto-guidance systems are precision agriculture technologies that use GNSS receivers and other sensors to help farmers navigate their vehicles and equipment around the fields accurately. These systems typically consist of a GPS receiver, a display unit, and sometimes other sensors (such as cameras or lasers) that provide additional information about the field and the vehicle’s position within it. Manual guidance systems (e.g., light bar systems) are relatively older technologies that have generally been replaced with more sophisticated automated guidance systems. The newer technologies provide for near-total automated steering of tractors, greatly freeing up farmers’ time in the cab
Farm Management Information Systems
A farm management information system (FMIS) is an important element of precision agriculture that supports the agricultural business’s decision-making process. FMIS are information and communication systems currently available for collecting, processing, storing, and disseminating data in the form needed to carry out a farm’s operations and functions. These systems have been designed as software (desktop computer-based) or apps (mobile device-based) and can be connected to the internet (internet/cloud-based).
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