The world’s population continues to grow and feeding it will require some creative problem solving. That’s why artificial intelligence (AI) and machine learning is becoming a hot topic of discussion among the agricultural community.
According to experts, earth’s population will grow to 10 billion by 2050, up from 7.7 billion today, and more than 820 million people are currently hungry. To add to this concern, global warming will have an impact on crops, further diminishing our available food sources. The power of AI to make farming more efficient and productive could be the answer to combating these growing threats.
Recently, Microsoft tested AI’s viability in agricultural applications in collaboration with the International Crops Research Institute for Semi-Arid Tropics (ICRISAT), developing an AI-based app for alerting farmers about optimal times to sow. In India, farmers tested the app as part of a pilot, and the experiment increased yield from the crops by 30%. This gain was achieved without additional capital expense.
Taking AI-powered farming apps further, sensors and machine learning can help farmers make smarter decisions about a number of activities – everything from disease and weed detection to soil and water management, to species breeding – all aimed at improving crop management and yield, and reducing cost and effort in the process.
And these benefits aren’t limited to the field – they’re low-hanging fruit, so to speak, for farmers who work primarily in greenhouses harvesting crops such as mushrooms, lettuces, microgreens, carrots and tomatoes, to name a few. Indoor farming offers a controlled environment which makes data collection and modeling easier and oftentimes more accurate.
The promise is imminent – Zion Market Research predicts that the global market for AI in agriculture will reach $2 billion, rising at a CAGR of 21% between 2018 and 2024.
Let’s take a look at these eight important applications of AI in agriculture in greater detail:
Precision agriculture: Of all the uses for AI in agriculture, Precision Farming is the most widely used, accounting for 35.6% of the global total in 2018. Precision farming is an approach to farm management that leverages data to ensure crops receive everything they need for optimum health and productivity. In this approach, on-the-go crop yield monitors perform spot harvest measuring that gives farmers insight into the strongest and weakest sections of their fields. They can then link this data to GPSplotted locations to create yield maps. AI and machine learning can augment this process by providing predictions about what effect changing certain variables will have on the outcomes. Machine learning algorithms can even be used to try different scenarios, and help farmers come closer to optimizing yield when certain variables or groups of variables change.
Matching yield with demand: As a central part of Precision Farming, today’s AI and machine learning technologies go beyond predictions based on historical data. They can now perform multidimensional analyses for yield mapping and estimation, to come closer to matching crop supply with demand. Data from sensors and cameras combined with computer vision technologies enable more precise forecasting, minimizing food waste.
Managing irrigation: Not only is the proper amount of water essential for optimal crop performance and yield, the environmental impact of excessive water usage — as well as the potential cost to farmers — can be huge. Using AI and machine learning, farmers can determine an ideal amount of water to use for a given crop, which can change depending on weather conditions, time of year and soil characteristics, among other variables. Even in controlled greenhouse environments, water requirements can change. AI algorithms help to identify changes so farmers can make adjustments.
Optimizing indoor farming envrironments: AI can be used to optimize various aspects of the indoor farming environment, such as climate, temperature, humidity, moisture and sunlight. Active environmental control requires continual monitoring via sensors and cameras that feed data back to a central source where AI algorithms such as Artificial Neural Networks (ANNs) and Fuzzy Logic Controllers (FLCs) can do their magic. Understanding what environmental conditions are optimal for various crops eliminates guesswork, helping farmers create an environment that delivers higher yield and better quality.
Detecting and preventing disease and weeds: AI and machine learning can be used to detect and eliminate disease and weeds that can impact yield and crop quality — and do so in increasingly environmentally friendly ways. For example, AI-powered robots can remove weeds mechanically, eliminating the need for farmers to use herbicides. These robots can be taught to detect and removed plants that show signs of disease, to minimize the impact to the crop. Apart from robots, data about the environment can be used to optimize for crop health and avoid the introduction and spread of disease in the first place.
Breeding specific plant species: Breeding various species of plants that thrive in a given environment or deliver on consumer demand for a specific food preference is typically a very tedious process that involves searching for and identifying genetic properties. Deep learning algorithms can help streamline and simplify the process by analyzing existing data about crop performance across different climates and environments, along with consumer purchasing behaviors relative to those crops. The results can be used to train a probability model that can accurately predict what genetic attributes will yield produce that both farmers and consumers prefer.
Analyzing and optimizing soil: Soil is part of the farming environment, but it deserves a separate mention, as it’s complexity requires more extensive analysis. The soil’s temperature, microbiome, water content and density, for example, are all critical factors to monitor and leverage for various plants. Machine learning algorithms can be applied to analyze evaporation rates and other dynamics that impact crops.
Deciding when and what to harvest: Rather than leaving the harvest to error-prone manual labor, farmers can use AI-powered robots to execute sophisticated and directed movements and pick only the fruits and vegetables in a crop that are ready for harvesting. This eliminates waste and maximizes yield by avoiding harvesting too early or unwittingly leaving behind produce that’s ripe for harvesting.
One AI Model Does Not Fit All Farms
The most popular AI and machine learning models being used in Agriculture today are Traditional Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs) and Support Vector Machines (SVMs). ANNs create a simplified model of the structure of the biological neural networks in the human brain, to emulate complex functions such as pattern generation, cognition, learning and decision making. Because such models can be used for regression and classification, they’re helpful in many of the applications in the Agricultural industry, such as disease and weed detection, and species breeding. SVMs are binary classifiers that can be used to for predicting yield and quality.
However, every application of AI in agriculture is unique, and considerable customization may be necessary to ensure the effectiveness of a machine learning or deep learning algorithm in any of the uses outlined in this post. Learn how 2predict can help you build and refine AI-powered solutions to help you increase crop yield, reduce expenses and run more efficiently.