Top 9 Benefits of AI in Agriculture

Agriculture is one of the most crucial yet unrecognized sectors for implementing next-gen technologies. This isn’t because the industry doesn’t have many use cases for advanced technologies; rather, the stakeholders there have the least options and research to help implementation. That being said, if there’s any industry that requires the most help today from technologies like AI, it is agriculture. 

According to a report published by the UN, the global population is estimated to grow to 9.7 billion by 2050, and there can be only 4% extra land available for cultivation to feed this population. This means food production will have to increase by 60% with no resource increase. Some technological miracles have definitely become the need of the hour, and the decision-makers are under a delusion about it. The government in Germany and Korea are incentivizing telcos for network development around the countries’ agricultural lands. Similarly, NIFA (National Institute of Food and Agriculture) of the USA is also investing in various programs to propel Midwest farming practices with digital agriculture technologies. 

But these efforts are clearly not enough to meet the colossal challenge ahead of global food security. In this post, we’ll discover several use cases where AI can tackle the challenge and make the agriculture industry more future-oriented.

AI Technology in Agriculture and Its Benefits

As we approach the fourth industrial revolution, we see most applications are based on either core or sub-domain of Artificial Intelligence technologies. AI is gradually transforming the world, and agriculture will be no exception. Here are some of the major ways AI is benefiting the agriculture sector. 

Precision Farming

While there is already a shortage of resources, the agriculture sector suffers most from inefficient utilization of the available ones. Mostly the misemployment of resources happens due to external, uncontrolled, and unknown factors, including weather, stress endurance of crops, climate change, degradation of soil, etc. While many of such factors remain unchangeable, AI tools can be employed to make the cultivation immune to them. 

For example, AI services company can use smart sensors to ascertain the stress levels in plants. The data thus collected is then fed into machine learning models to understand the signs of stress in plants and the most effective methods to overcome the same. With this, more precision can be made around a crop’s cultivation time and place.  

Similarly, large datasets can be collected through real-time soil, harvest, and crop inspection for yield mapping. And then, the same data can be used with AI-based predictive analysis applications to forecast the potential yield rates for different crops, at different times and places, with different supplementing chemicals and irrigation supplies. By ascertaining the factors for the maximum yield, crop planning can be further enhanced for more promising results.

Read More: Importance of Modern Agriculture Technologies in Farm Management

Cost Savings

Farmers can ensure maximum yield with the same resources, time, effort, and expenditure with precision savings. This does not only help them increase their net profit but also helps in cutting insurance costs thanks to the data-driven methodologies they adopt. Other than that, they can even cut down on their resources and diversify the same with the help of the model by ascertaining the minimum amount required for a good harvest. 

But precision farming is not the only method that brings the cost benefits of AI in agriculture. Automation of various tasks through smart equipment and robots also saves them money on labor costs. These smart machines are known to bring unprecedented accuracy to the work processes. This means the farmers can save huge money on herbicides, irrigation, and cultivation techniques by fine-tuning the process for optimal utilization. All this leads to huge cost savings, making farming a lucrative area again, which could eventually decrease the churns in the industry. 

Tackles Labor Shortages

As mentioned in the previous point, agriculture is facing major employee attrition because of the labor-intensive nature of the job and ever-decreasing remunerations. While many policies are being run by governments to keep skilled labor engaged in their jobs, those are not enough to tackle the current challenges of labor shortage. Tailored enterprise solutions are required to solve this at root level.

Something needs to replace the lack of workforce, and AI would just be the right tool to do that. With smart robots and equipment with the cognitive abilities of AI, many manual tasks can be handled with greater efficiency. Smart irrigation, driverless tractors, agribots, and packaging bots can easily help farmers who are struggling to find the right workers who learn and stay back on the job as they scale their operations. 

Now agriculture work is hard, and policies will have to be really beneficial to keep the employees in. But till the time that doesn’t happen, AI-based smart robots can take over those tasks. What’s interesting about handling labor shortages with AI is that there is a scope to generate better productivity due to the inherent accuracy and resilience of such tools, as compared to their human counterparts.

Price Forecasting

Farmers have always had trouble deciding which best crop to grow to get the best ROI. Until now, they were only relying on the local information and the high-value crops of the preceding period. As is evident, both these sources are not enough to create a profitable harvest. More variables need to be processed to understand the underlying patterns to accurately predict high-demand crops for the coming year, so farmers can make the right financial decisions. 

This is where the predictive analytics capabilities of AI enter the picture. A model can be created to study factors like weather, price trends in the market, both domestic and international, demand and supply rates, and the cost of production. Historical data on all these factors can then be fed into ML algorithms to train it on the historical prices of a given crop. This model can then evaluate the current data to predict price with greater accuracy, based on multiple variables that traditional methods could not have considered. 

With price forecasting, the administration can also decide the subsidies for more crucial crops with lower price indexes and high production costs. This way, farmers can be assured of getting the ideal returns for their efforts, and governments too can establish better food security in the region. 

Crop Health Monitoring

Like price, the health of a crop too depends on a variety of factors. From the nutritional content of soil and weather to the probability of pest invasion and the endurance levels of the crop, multiple variables can be taken into consideration to determine the ideal health conditions for a crop. Based on these details, models can be trained to calculate the probability of crop deterioration even before planting the seeds. This way, they can take precautionary measures as required to reduce risk factors with minimal investments. 

Smart sensors can also be introduced to monitor the health of crops in real time. As mentioned before, farming is not easy, and the workforce can easily overlook the signs of ill-health in the cultivation, like discoloration of leaves, the presence of harmful weeds, and microscopic signs of pest presence. However, with AI-based health monitoring systems, sensors can detect signs of decline in crop health before they reach irreversible levels. Farmers can then take corrective measures to improve and salvage their crops from insect-borne diseases, weather-based stress, and lack of nutrition in the soil. Such custom AI development in real-time image recognition can help agriculture minimize waste and achieve greater resource optimization. 

Supply Chain Optimization

Many industries, including agriculture, are benefiting largely through custom AI software development services in supply chain optimization. These applications can use RFID and IoT sensors to make the supply chains more transparent and traceable. With traceability and an AI-based monitoring system, both the quality of the product and the delivery timeline at each touchpoint can be audited in an automated fashion. 

This way, bottlenecks can be reduced by discovering their sources in real time and removing them through standard procedures. What more? Suppliers can more accurately predict the availability of fresh stock through AI-based track and trace software to make better pricing decisions. 

Businesses can also streamline the distribution of fresh farm harvests by accurately predicting the demand and evaluating it against the availability in the nearby centers. This way, both understocking and overstocking can be resolved through an efficient system that relies only on data-driven models.

Read More: How to Bring Digital Transformation To Your Supply Chain Management?

Enhanced Irrigation System

Irrigation has largely remained an area that depends solely on the experience of the farmers and the availability of resources. Now while water resources cannot be changed in real-time as per requirements, through predictive water requirements and weather forecasting, farmers can plan irrigation in a much more effective and efficient way. Water is one of the scarcest resources around the globe today, and the situations are only expected to become tenser in the future. As such, planning optimal water utilization has become the need of the hour, and custom AI-based farming solutions have become really helpful. 

Other than planning, real-time irrigation can also be implemented to meet the requirements of the crop without even the presence of an experienced farmer. AI-powered smart irrigation systems can use sensors to check the humidity in the air, moisture in the soil, and past data on the most effective irrigation frequency to automate irrigation-related processes. As such, the replacement of manual irrigation with automated tools will not only result in improved productivity but also in enhanced quality of the harvest, thanks to the accuracy of irrigation operations. With this the future of AI in agriculture is bound to be more bountiful in all areas. 

Improved Pest Management

Did you know that every year, 20 to 40 percent of global crop production is lost to pests? Pests have been destroying $70 billion worth of crops annually. And despite all the technological and scientific advancements, we have not yet been able to curb this destruction. 

However, some groups of researchers are confident that this time-honored challenge can finally be resolved with custom AI development. One group has been working on commercial crops like cotton, where they’ve set up pheromone traps to accurately detect the presence and volume of pests affecting a given area. They then test it against the Economic Threshold Limit (ETL) to make a data-driven decision on the spraying of pesticides.  

Similarly, another group is working crops that are affected by various pests, like potatoes. They, too, work with sticky traps to accurately identify pests like bees, moths, chafers, fruit flies, and mosquitoes. This helps farmers use the correct type and amount of pesticides in any given region. This method is far more effective than the traditional and manual efforts of pest control in agriculture. 

Crop Diseases Prevention

Similar to the detection of pests’ presence, you can hire an AI software development company to use the image recognition abilities of AI to detect ill-health signs on crops. What’s more, these sensors don’t even have to wait for visual signs of ill-health in a plant. All they need is a regular inspection of the soil quality, pests, or disease spread information from the vicinity. 

As such, AI systems can quickly pick on the pattern of the spread and the invisible signs of health deterioration to keep a close eye on the plants’ health in an automated fashion. While such an application is not yet widely used in real-life cases, researchers around the globe are actively participating in finding the most efficient usage of AI in detecting and preventing diseases in crops like tomatoes. Many researchers have already created models that can accurately detect diseases with an accuracy of over 90% to prevent spread well in time.

Conclusion

Once we get a clearer view of the impact that AI can have on agriculture, we can see that the benefits of AI in agriculture are incalculable. Since the industry mostly functions on large-scale operations, the beneficial effect of AI becomes exponential. Smart farming tools like driverless tractors, automated irrigation supply systems, health monitoring sensors, and predictive analytics for weather and pricing can tackle many of the ubiquitous challenges in the industry, including labor shortage, the imbalance between supply and demand, and resource misutilization. 

Interestingly, despite all these potential benefits, only a select few groups and countries actively invest in AI development services to tackle their agricultural issues. Mostly it’s because they’re unable to either discover the ideal AI use cases for their specific requirements or find a technical partner that can produce custom solutions.  

Matellio, with its years of experience in the arena, is a leading enterprise software development company. With our team of AI developers specializing in creating tailored solutions, we are the ideal partner to bring your custom solutions in AI in the agriculture market.

In short, AI is nothing but a simulation of human cognitive capabilities with the additional advantage of faster computations, and we’re more than equipped with skills to leverage that for you. All you need to do to see your business’ future of AI in agriculture is fill out this form and book a free consultation. Our experts will take it from there, creating the project plan, estimated delivery timeline, and a quote, all for free.