5 Use Cases of RPA in Agriculture: Farm More Efficiently in '24
The world population recently reached the 8B mark. And by 2050, it will be almost 10B. A growing population dictates increased food production for sustenance.
The agricultural industrial; however, is not the most sustainable nor the most efficient. They are responsible for:
Given that the scale of agricultural efforts should increase to meet the growing food demand, we can expand its current environmental shortcomings to increase.
In this article, we will explain the top 5 use cases of RPA in agriculture.
1. Manure addition
RPA bots can send out alerts whenever the soil is ready for manure addition.
The consensus is that early Fall3 is the best time to add manure to the soil. RPA bots can send notifications during the mid days of September, reminding the farmers to add manure.
Applying manure at the wrong time or in the incorrect quantity can have detrimental4 effects on the crops instead of nutrition. By scheduling automated reminders, the guess-work is taken out of manure addition.
Moreover, nutritional-wise, no two manure are the same5. If there are structured sets of data on:
- All the specific crops on the land,
- The precise composition of the soil,
- And the manures’ nutrition level,
The farmer can create business rules engines for the quantity and type of manure that should be applied to each farm terrain and crop. The RPA bot will follow the rule-based commands and send the notifications accordingly.
2. 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,
- 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 farmer precisely knows:
- Which patch of his land comprises what kind of soil,
- The soil’s current nutrition levels,
- And what they need,
He/she 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.
RPA bots can schedule smart irrigators 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 (i.e., 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. Moreover, overwatering can suffocate6 crops, cause costly water bills, and unnecessarily deplete water resources.
Smart irrigation can be the answer to those challenges. We weren’t able to independently confirm the number, but reports suggest that smart irrigation sensors could save 20%7 more water than traditional methods.
RPA bots can allow farmlands to use their water resources more efficiently and irrigate their lands methodologically.
Not all crops should be harvested simultaneously8.
Bell peppers, for instance, should be harvested just before they will be eaten. Zucchinis should be harvested when they reach a specific size. Otherwise, they’d just keep on growing and lose their taste.
The specific timings of harvests also differ:
- Vegetables should ideally be harvested in the mornings9
- Grape wines, corn, tomatoes, garlic and onions are usually harvested at nights11 in test weight reductions or mold growth (in case of excessive rainfall). Harvesting too early might result12 in immature seeds and low moisture content. At either end of the spectrum, untimely harvesting jeopardizes crop quality.
Logistically as well, late harvests might disrupt pre-scheduled transportation to distribution centers, farmers’ markets, and retail stores.
Depending on the type of the crop, between 20-50%13 of the food is perished during transportation. On-time harvests minimize the likelihood of crops going back while in transport because they were harvested and shipped close to their perishing date.
RPA bots can connect to IoT sensors that are monitoring the crops’ weight, the sunlight, and other variables to gather those data. With their OCR and NLP capabilities, they can then read the information, cross-reference them against their knowledge base, and specify when harvests should be done automatically.
5. 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:
- 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.
For more on agriculture automation
To learn more about other applications of automation in the agricultural industry, read:
- Top 5 Computer Vision Use Cases in Agriculture
- 3 Ways Digital Transformation Improves Agriculture
- Top 5 Use Cases & Best Practices of IoT in Agriculture
You can also learn more about 100+ RPA use cases. And to get a comprehensive look into RPA, download our RPA whitepaper:
If you believe you would benefit from adopting an RPA solution, visit our data-driven list of RPA vendors.
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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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