Living in yesterday’s science fiction: Artificial intelligence in agriculture

In today’s rapidly evolving technology landscape, one increasingly common theme continues to be Artificial Intelligence (AI) and its role in our society. AI is changing everything from the way we shop with products like Amazon’s Echo using voice commands to initiate the purchase of products while other AI devices like Nest keep our homes safe and comfortable. These devices represent a new type of “smart” technology that utilizes AI or machine learning. Machine learning distills large amounts of input data into algorithms based on patterns. The amount of investment in the field of AI has grown substantially spanning all economic sectors ranging from industrial to consumer goods, health care and even banking. Technology titans such as IBM, Microsoft, Google, Amazon, and Facebook are committing heavily to continuing development of AI.

To follow are three examples of how AI or machine learning is applied in other areas and how it could be used by producers in the near future.

Maximizing input

One example, AlphaGo, created by Google subsidiary DeepMind, is increasing data center efficiency by 15%. The program analyzes inputs such as power for running data center equipment and cooling systems, and then calculates how changing different factors, such as ambient temperature settings or reprogramming power distribution, affects total power usage.

Similarly, AI could analyze all the inputs of agricultural production such as hybrid performance based on historical yield and disease pressure. AI may also help with selecting plant populations based on weather models, soil types and topography, or nutrient and water availability.

If there is one area that the digital agriculture revolution has given producers, it is a mountain of data. AI will help producers interpret this data and extract actionable information with which to enhance profitability while ensuring the sustainability of their enterprises.

Directing soil sampling

Another opportunity for agriculture could be AI-directed soil sampling. NASA is using AI to direct deep space probes and rovers to increase sampling efficiency. Instead of employing a ‘blind targeting’ method where the rover randomly samples an area, AI is used to direct sampling based on analysis of environmental indicators to select the most beneficial sampling locations. Instead of randomly sampling fields for nutrient availability; AI can be used to direct sampling practices to regions of the field that first exhibit deficiencies. Early diagnosis of nutrient availability problems, prior to full field involvement, could lead to higher yields or lower treatment costs.

Optimizing disease diagnosis

A third application could be in crop stress diagnosis. In looking at IBM’s Watson and the role it plays in the medical field, AI could be extremely powerful in aiding crop scouts and plant pathologists to quickly and accurately diagnose crop disease.  Watson provides treatment recommendations that the doctor can review and select from. It is unreasonable to expect a physician to comb through the latest medical research from around the world to identify the best treatment for their patient. There is simply too much new information generated on a daily basis for practicing medical professional to remain current in their field. Watson does this for them by analyzing new information, and searching for relevant patterns that match the patient’s symptoms. In this way, it provides the physician the most pertinent information thereby expediting an accurate diagnoses. In a similar fashion, AI will give the agricultural producer, or crop scout, access to the latest, pertinent research to better monitor crop health and implement cost-effective disease treatments.

Enabling agriculture

These are just some of the ways in which AI could be deployed on farms in the future. However for the time being, AI enables pattern recognition through machine learning. It is likely that AI will enable producers to optimize inputs and asset use for the highest economic benefit without having to spend hours in front of a computer — in my opinion the greatest potential for AI in agriculture.

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