Automation tools, such as RPA, can automate some back-office tasks in the agricultural sector to rationalize the processes. Let’s learn the top three use cases of RPA in agriculture, shared by Bardia Eshghi, an Industry Analyst at AIMultiple.
Soil Preparation
IoT sensors in the soil can gather its data and send it to the cloud. The RPA bots then can extract the data, structure it, transport it onto a template or a spreadsheet as a report, and send it to the farmer at specific intervals. Different soil types require different preparations. Sandy soils, for example, are lower in nutrients, as opposed to clay soils. This means their nutrition supplements should be in respect to their specific properties.
If the farmers precisely know which patch of his land comprises what kind of soil, the soil’s current nutrition levels, and what they need, they can prepare the soil in a more data-driven and tailored manner to the soil’s needs, with respect to the specific amount of nutrients that it needs to nurture healthy crops.
Irrigation
RPA bots can schedule smart irrigates to start the watering process whenever the sensors indicate that the moisture level has fallen below the acceptable threshold for each crop and soil patch. The RPA bots can also be programmed via screen recording to scrape precipitation rate and schedule the irrigation phases in advance. For instance, if there’s heavy rainfall, irrigation could be pushed back.
By irrigating the crops in an efficient and data-driven manner, water usage can be curbed. Not to mention that overwatering can suffocate crops, cause costly water bills, and unnecessarily deplete water resources.
Yield Prediction
RPA bots can extract information from different datasets and feed them into ML models to predict the yield. Yield is a function of weather, soil condition, seed and fertilizer type, crop weight, and more. RPA bots can scrape and extract this data directly from websites thanks to agriculture APIs, and from IoT sensors, to input into ML algorithms.
The outcome will be a yield prediction with minimal human intervention. And because a machine makes the prediction, the analysts can feed the model with as many relevant variables as possible to increase accuracy. However, this is a sophisticated model that doesn’t depend on RPA alone but on ML, deep learning, neural networks, and more.
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