Using Al and ML to manage complex databases in agriculture


By Mallory Dodd, Senior Solutions Architect, iMerit Technology

Agriculture is the backbone of several economies, including India’s. With the rising global population, projected by the United Nations to increase from 7.5 billion to 9.7 billion by 2050, the pressure on the agriculture sector to increase productivity is greater than ever. This means more efforts are required to empower and enable the sector to produce more, faster.

In India, which still practices traditional farming, there is a growing need to innovate and incorporate technology to a greater extent. Modern farmers and other stakeholders are seeking newer ways to increase production and minimize wastage. As a result, the sector is looking at artificial intelligence (AI), machine learning (ML) and relevant data-driven technologies to drive the agricultural revolution. AI- and ML-powered solutions will not only enable the sector to increase yields, but will also reduce production costs, improve quality and accelerate go-to-market strategies.

A data-driven approach
Traditionally, farmers rely on physical inspection of massive fields over long periods of time to find out whether their yields are unhealthy or infected, as well as study their growth. With AI- and ML-driven systems, farmers can use data-driven insights to study the yield in a fraction of the time and take necessary steps more quickly. Technology-powered tractors or unmanned aerial vehicles (UAVs) can assist farmers to collect crop data in less time, with higher efficiency.

A data-driven approach results in better extraction of information from farms. However, this data comes with a lot of complexities that are difficult for humans to comprehend and deal with. AI algorithms are designed to effectively extract insights and enable decision making for farmers. With these systems, farmers can predict yields, evaluate crop quality, detect abnormalities, and take relevant measures.

Farmers can also log in to customized dashboards on electronic devices to access accurate assessments of harvestable versus non-harvestable acres at any point. The maturity and weight of harvestable crops can also be measured and predicted.

Types of agricultural data
Agricultural data can be collected in several ways. The key data for farming and related operations include:
● Weather data: Collected from satellites and sensors to understand weather conditions and forecasts. For example, air pressure and humidity are used to calculate probable rainfall.
● Crop data: Collected from agricultural vehicles and UAVs/drones. This data is key to providing insights about the condition of the land, nutrients it contains,or the amount of fertilizer used.
● Machinery data: Can be collected by studying the use of machines on the farm to understand the effectiveness for crop yielding.

The challenge
Extracting useful insights from data for decision making isn’t easy. For example, the main purpose of herbicides, water, and nitrogen fertilizers are well established. They are required in specific quantities for better production and reducing challenges with regard to the land or the crop. Moreover, this data is variable and dynamic, depending on soil conditions, weather patterns, crop health, etc. Experts cannot address the complexities of agricultural problems fully as the sheer quantity of data is too massive for human brains to easily synthesize on their own.

Benefits of AI and ML
The main goal of enabling AI for agriculture is to manage large volumes of complex data to gather insights and build solutions more quickly. AI and ML can help farmers with data management in multiple ways:.

● Analyzing data: This will help farmers predict weather and devise solutions to lessen the damage from poor climate conditions. AI can help to analyze rainfall patterns over a period of time and to predict future rain events. Wind and air pressure, key indicators of adverse weather conditions, can also be analyzed and stored as data to make better predictions based on algorithms. Using ML algorithms in connection with images captured by drones and satellites, AI can predict various weather conditions.

● Detecting pests and diseases: AI trained with computer vision techniques can detect fungal infections that may threaten crops. Once trained, AI engines can intake images and video of crop leaves, fruits, and stems collected from the field and tag potential dangers such as fungi, insects, and more.

● Soil health monitoring: AI can analyze steady streams of data to monitor soil conditions and detect nutrient deficiencies on an ongoing basis, and provide automatic alerts whenever conditions change beyond defined thresholds

● Decision-making: Analytical insights provided by AI systems can help farmers and stakeholders make informed decisions about water management, crop rotation, timely harvesting, types of crops grown, optimum planting, pest attacks, nutrition management and more.

AI cannot replace the knowledge an experienced farmer has gathered over the years. However, it has the potential to complement traditional farming methods and improve agricultural practices. Deep collaboration with data-driven technologies will enable farmers – especially in a fast-growing country like India – improve yields, empower themselves and their business, and help tackle food scarcity for decades to come.


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