LinkedAI

Modern agriculture generally refers to the implementation of farming innovations and techniques with the use of technological tools and reliable data in order to boost efficiency. By analyzing real-time sensor data and comparing these with historical trends, agriculturists can make insightful decisions on tasks like performing intelligent spraying, implementing crop rotation, developing better tillage practices, and in general, improving crop yield.

Automated agriculture, also known as farm automation or smart farming, is one approach to modern agriculture that utilizes drones and robotics to create autonomous tractors, seeders, harvesters, and waterers in crop production. These technologies work together with AI subfields such as deep learning, computer vision, and machine learning, not only to automate farming processes but also to make better predictions.

Intelligent robotic spray fertilizer on plants

For instance, deep learning in agriculture can process years’ worth of raw field data such as crops’ performance in various climates or identifying certain leaf defects — and use this to create a probability model. Then machine learning can utilize the available information to further narrow the search, and develop a more accurate model that helps predict improved crop yields, identify plant diseases, and more.

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The AgTech market in North America reached a noteworthy 6.2 billion US$ in the year 2021 alone. This isn’t surprising at all considering how beneficial machine learning has proven to be in modern agriculture. Here are five of its top applications:

Crop Management. Crop management involves so many pre-harvesting activities that could decide the success of a future yield. Machine learning models are used to analyze patterns for dealing with the challenges in the agricultural lifecycle such as frequency of drought or higher temperatures, and for enabling informed decision making on what crop species to grow in particular seasons.

Yield Mapping. Machine learning in agriculture is also a key basis for a farming technique known as yield mapping. Using digital imaging tools for ‘sensing and mapping’ activities, agriculturists are able to have an idea of the field’s yield even before a vegetation cycle commences. Yield mapping may also involve learning from a field’s historical data to see which areas are best or least conducive for crops.
Pest Detection. Pest detection is more easily achieved with a combination of in-ground sensors and drones data. While applying pesticide is nothing new, the process not only kills pests but also other insects in the area. Now, using computer vision models that accurately recognize pests from legitimate insects, farmers can better identify and capture or use pesticides on the ‘bad actors’.

Irrigation System Management. AI models also contribute to managing irrigation systems — detecting leaks, finding ways to optimize systems, and measuring how high yields can go with effective crop irrigation. Water is a scarce resource in many places, and effective irrigation management will not only aid farmers but the whole area as well.
Automation of Tasks. The high cost of labor and lack of workers are part of why agricultural operations have been struggling. The entry of automated agriculture through the utilization of robotics for tasks such as weeding, harvesting, and fertilizer spraying is thus a major factor that can increase productivity and profitability in farmlands.

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The contributions of deep learning, computer vision, and (especially) machine learning in agriculture are already recognized, and the applications can only become more groundbreaking and complex moving forward. Data-driven approaches promote accurate and insightful decision making that, in turn, leads to improved efficiency and higher productivity.

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How Important Is Machine Learning in Modern Agriculture?