Precision agriculture: How machine learning simplifies farming

As long as the population of the world keeps increasing, so will the consumption of food. Already more than 820 million people around the world are currently hungry. Therefore, food production rates need to increase by as much as 70%.

To keep up with the population growth rate, the agricultural sector will require some creative problem-solving mechanisms. And that’s where artificial intelligence (AI) and machine learning (ML) comes in.

AI farming and machine learning technologies cut through the entire crop growing and harvesting cycle. Farmers can access more accurate data and analytic tools to navigate environmental challenges and improve crop production, from accurate sowing time and good seedlings to soil preparation, greenhouses, water feed measurement, and harvest. There are several things farmers can do with ML tools to simplify farming. With so much promise available through AI technologies, let’s consider some of its applications.

1. Precision and yield prediction

When it comes to AI and agriculture machines, precision farming is the most widely used. With the use of AI, farmers can get the precise data that tells them everything crops require for optimum health and productivity. Farmers also gain insight into the strongest and weakest sections of their farm fields. It, in turn, enables an improvement in yield prediction.

Beyond the use of simple prediction based on historical data, there are AI crop definition computers that provide data on the go. They also offer a comprehensive, multifaceted analysis of crops, the weather, and supply and demand levels. These analyses enable farmers to make the most of their yields for larger populations. Additional data captured bay visual technologies like sensors, drones, and cameras allow precise forecasting and curtail food waste.

2. Irrigation management

Water is an essential aspect of crop vegetation. And only the proper amount of water is needed for optimal crop performance and yield. A little too much or less can result in low yields and huge costs. With the help of AI and ML, it’s possible to determine the ideal amount of water a crop requires. These tools can identify such changes irrespective of alterations due to the weather, season, or soil characteristics and provide the right information for farm irrigation management.

3. Disease & weeds detection and prevention

Whether it is an open farm or a greenhouse, farmers often use chemicals for disease and weed control. The amount required can be expensive, make farm produce unsafe for human consumption, and environmentally unfriendly. But with AI and machine learning, easier detection and harmless elimination of plant diseases and weeds are possible. More so, it is done in an environmentally friendly and organic manner.

For instance, AI-powered robotic agricultural machines can mechanically weed the farm. Thus, it eliminates the need for herbicides. These robots are programmed using ML to detect and weed out plants that show signs of disease. Furthermore, agro-chemicals are still sometimes needed. In such situations, ML robotics using drones and precision agriculture management targets the time, place, and affected plants.

4. Breeding specific plant species

Multiple crop species breeding can be a tedious process. Farmers look for the different species of plants that thrive in an open or closed environment. For example, species with higher disease resistance, fewer resource requirements, more nutrition, large, redder, and greener appearance.

And likewise, species that deliver on consumer demand for a specific food preference. Manually, searching for the best species might take a too long time and process. ML crop machines, with their vast learning algorithms, will help streamline and simplify the breeding process.

It does this by analyzing available data regarding how crops perform in various climates and environments. Consumer purchasing behaviors that are relative to all crop species are also considered. The results of the analysis can provide a probability model. Such a model can accurately predict the genetic attributes that will produce profitable and preferable products to farmers and consumers.

5. Analyzing and optimizing soil management

Another essential aspect of crop cultivation is soil management. Soil temperature, density, microbiome, water content, etc., are critical components that should be monitored before and when planting. These components can be tricky and vague.

When applied, machine learning algorithms help to analyze water evaporation levels, soil temperature, and moisture. It can likewise help farmers understand other dynamic compositions that impact crop production.

6. Improves harvest decisions

The use of manual labor on farms can be tricky. There are instances whereby the pickers might harvest produce that is not yet ripe, and such a harvest can lead to waste. So, instead of using error-prone manual laborers for harvesting, using AI-powered robots cuts down the error rates.

For the harvest, AI crop machines can execute sophisticated and direct movements by picking only the ripe fruits and vegetables. In this manner, farmers record less waste and maximize yield by avoiding too early harvest. Also, the probability of unwittingly leaving behind fruits or vegetables that are ripe is eliminated.

7. Agricultural robotics and digital workforce

The level of physical farming is dwindling, and fewer people apply for jobs as farmhands. This is often due to physical and strenuous labor. AI and machine learning have stepped into solving critical farm labor challenges. With these robotic and computer-based technologies, farming becomes augmented and cushions the low turnout of traditional farmhands.

The beauty of the agricultural robotics workforce is speed, accuracy, cost reduction, and risk. Farmers are given analysis that can present permanent solutions for a workforce that fluctuates and is unpredictable. It is often easier to compare the two and see the result of using farming machines. However, blending both the digital and manual workforce is more advisable for smart farming.

Similarly, more farmers are taking advantage of chatbots for advice and recommendations on specific farm problems. These chatbots are successfully present in other industries such as health, shipping, banking, online retail, etc. Thus its usefulness in farming does not come as a surprise. AI farming and ML cognitive technologies aim to empower farms everywhere to run more efficiently. And to produce the fundamental produce that meets demand tastes and dietary lifestyles.

8. Optimizing indoor farming environments

With the increasing level of advocacy to preserve our planet, more farms are shifting towards greenhouses. Using AI and machine learning, farmers can regulate several aspects of their indoor farming environment. For example, things like climate, humidity levels, temperature, moisture, and sunlight can be measured and controlled. It enables the crops planted in the greenhouse to reach the harvesting stage much faster yet retain as many nutrients as possible. The soil moisture and control help regulate the water supply, so the plants get the correct water amount.

These greenhouses also benefit from an active environment using continuous monitoring through sensors and cameras. The data from these tools are sent to a central source, so AI algorithms like Fuzzy Logic Controllers (FLCs) or Artificial Neural Networks (ANNs) can draw up accurate analysis. The results will help the farmer understand if environmental conditions are optimal or not. And also what works for each species, so guesswork is eliminated. It will let the farmer know how and when to dispense these solutions accurately.

Conclusion

With the right information and technology, farms can increase supply output to match demand. The quality and quantity of harvest can also be improved using AI predictive measures for seedlings, soil management, water irrigation, weed, and disease control. Each farm’s needs are unique, and as such, the application of AI agricultural machines will require considerable customization levels.

These opportunities available in AI farming and machine learning technologies are by no means exhaustive. As technology advances, new solutions are emerging to help simplify agriculture and increasing productivity and consumer satisfaction.

About the author: Jamie Fry – Purposeful and promising author. At this moment, he is working at writing services review companies, as Pick the writer and Writing Judge and enhances his blogging skills. Confidently goes to his goal. He has a talent for writing original content. The main conviction in his life: To be the best in the field in which you are developing. Always in search of fresh ideas.

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