Agritech - Big Data

Big data refers to vast amount of both structured and unstructured data generated from digital sources. In agriculture, it makes use of sensors, weather patterns, drone images, satellite images, and market conditions to collect data.

Applications of Big Data in Agritech

• Precision Farming: Big data provides insights on various resource inputs like water, fertilizers, insecticides and pesticides. By using data from sensors, big data provides information on where resources are needed, in what quantity is needed and when is needed. This helps in preventing overuse of resources like water and pesticides.
• Predictive Analytics: Big data help in predicting crop yield by analyzing weather patterns, soil health, and historical farming data. Analyzing crop yield help farmers in better planning of market demand and arrange required resources accordingly. It also helps them in risk management by predicting potential threats.
• Livestock Monitoring:Big data also helps in livestock monitoring and management. Data is collected using wearable devices, sensors and IoT devices. They track real-time data animal health, movement, and feeding patterns. All these collected data is then used for health monitoring, feed optimization, and improving farm productivity. For health monitoring, Big data uses data of body temperature, heart rate, and movement to detect early signs of any illness.
• Weather and Climate Prediction: Big data make use of real-time aerial and sensor data for accurate and precise weather and climate forecasting. Predicting weather patterns helps farmers in making planting decision i.e in deciding when to plant crops with minimum risks and making irrigation schedules based on rainfall.
• Soil Health Monitoring: Big data makes use of real-time soil sensors to collect data on soil moisture levels, nutrient content, and pH. Analyzing these data it provides recommendation on fertilizer management, water usage, and long-term soil health.

Data Collection and Storage Techniques

• IoT Sensors: IoT sensors are placed in the field to monitor soil moisture, temperature, nutrient levels, and crop health. Sensors on livestock monitor their movements, feeding habits, and overall health. The data from these sensors are fed as continuous streams of data into centralized systems.
• Satellite Images: Satellites captures high-resolution images of large areas which helps in monitoring crop health and weather changes. Satellite's data is valuable for predicting broad geographic patterns, such as droughts or pest infestations.
• Market Trends: Big data includes data on market prices and consumer choices and preferences. These data helps farmers to schedule their production according to market conditions.
• Weather Stations: Weather station collects real-time data on temperature, humidity, and, rainfall. Based on these data farmers can manage and schedule agricultural work effectively such as irrigation, harvesting and planting.
• Drone Images: Drones monitors and collects data on crop health and soil quality. Drones are equipped with advanced cameras and sensors and they fly over specific farm areas capturing quality image data.
• Cloud Servers: Cloud servers are used for storing collected data. Cloud platforms provides easy access and analysis of stored data. It provides flexibility as farmers can access their data from anywhere.
• Local Databases: It uses local databases to store the collected data. It is used in areas with limited data connectivity. It has limited access and real-time analysis of data compared to cloud servers.

Benefits of Big Data in Agritech

• Low Risk: There is a low risk of loss as every decision is data-driven. Farmers have access to data about weather conditions, disease outbreaks, and pest infestations. With available data, they can avoid crop loss due to pests, climatic conditions and other factors.
• Increased Profit: Big Data helps in reducing input costs by optimizing use of water, fertilizers, and pesticides. It helps in preventing over-application of resources and reducing waste. This helps farmers in cutting down on expenses and increasing their margins.
• Improved Efficiency: Analysis of vast amount of data helps farmers in using their resources efficiently and making optimum decisions. For example: They can decide on amount of fertilizers to use, when to irrigate which section of field and how much water is needed for irrigation.

Limitations of Big Data in Agritech

• Data Security: Since data is collected from various sources and various farms issue of data privacy may rise. Farmers may be vary of sharing sensitive information and concerned about data loss.
• High Cost: Installing Big Data systems can be costly. It requires high investment in infrastructures such as sensors, drones, and weather stations.
• Technological Barriers: Farmers of rural areas or undeveloped areas may not have access to advance technologies like high speed internet connectivity, advanced machines, and sensors which forms core component of Big Data analytics.