They say the risk is inevitable in the global business world, but your high resilience in facing challenges can iron out the wrinkles in today’s competitive, thriving market.

Currently, trends are changing at a lightning speed and if we have to pick one core industry that is exposed to facing heavy risks, without a question, it is our agriculture industry, a major source of food supply for the global world population, particularly yielding healthy crops and catering basic needs, all round the year.

We have been taught since childhood about the concept of what you reap is what you sow, but my friend, the practical world does not go by the book. They may have forgotten to add “if you are lucky enough”.

In the real world, people have to deal with changing climatic conditions including unexpected rainfall or thunderstorm, drought, deforestation, humidity, pollution, and other environmental hazards. They drastically affect the crop yield and livestock of a country. In the aforementioned scenario, we can only empathize with our farmers and understand the pressure of managing healthy crop production singlehandedly.

Furthermore, the worst scenario entails weed or pest/bacteria production, crop struck by a deadly disease, global pandemic affecting the food supply chain. These are all unfavorable conditions that can get farmers in dire straits to manage labor-intensive processes that are not automated yet.

On the contrary, the population is growing at a massive rate with urbanization going at its peak. Consumptions patterns are burgeoning over time along with a surge in disposable income. The pressure on farmers to meet the rising demand of existing consumers is indeed a tough row to hoe.

According to experts, the earth’s population will grow to 10 billion by 2050, rising up from 7.7 billion today, and more than 820 million people are currently hungry. According to a NASA study published in the peer-reviewed journal Nature Food on November 1, 2021, global climate change might disrupt maize (corn) and wheat productivity as early as 2030 under a high greenhouse gas emissions scenario. Maize crop yields are expected to fall by 24%, while wheat yields are expected to rise by roughly 17%.

The power of AI to make farming more efficient and productive could be the answer to combat these growing threats. AI has the potential to transform the way we think about agriculture, allowing farmers to obtain better outcomes with less labor while also providing a slew of additional advantages.AI does not work independently rather a creative and novel merger with other tools and technologies can enhance traditional farming methods to innovative ones.

It’s high time to digitize agriculture and increase the yield of farmed acres through AI. This article will primarily focus on numerous promising AI technologies revolutionizing the agriculture sector.

What are the applications of AI in the agriculture/farming industry?
Let’s check out how farmers and the world can reap out potential benefits using AI.

Applications of AI in Agriculture

     1. Precision Farming

Precision farming, a type of farm management that uses data to make sure crops have all they need to thrive and produce at their best. On-the-go crop yield monitors have been using this method to do spot harvest measurements, giving farmers information into the strongest and weakest parts of their fields.

They can then construct yield maps by linking this data to GPS-located places. AI and machine learning can help with this by predicting the impact of changing specific variables on the outcome. When particular factors or groupings of variables change, machine learning algorithms may be used to test new scenarios and help farmers get closer to optimal production.

Precision agriculture uses artificial intelligence (AI) to help in the detection of plant diseases, pests, and inadequate plant nutrition on farms. Artificial intelligence sensors can identify and target weeds, then determine which herbicides to use inside the appropriate buffer zone. This helps to prevent the overuse of herbicides and the accumulation of toxins in our food.

     2. Improves Decision Making – What and When to Harvest

Farmers may employ AI-powered robots to conduct complex and guided operations and pick only those fruits and vegetables in a crop that are ripe for harvesting, rather than relying on error-prone, labor-intensive work. This reduces waste and increases productivity by avoiding harvesting too early or unintentionally leaving ripe vegetables on the vine.

Predictive analytics has the potential to pave the way towards modern farming techniques. Farmers can collect and analyze substantially more data using AI than they could without it. Farmers may use AI to handle critical challenges including evaluating market demand, estimating prices, and calculating the best time to sow and harvest.

Moreover, AI can help farmers acquire information about soil health, provide fertilizers suggestions, check the weather, and track the maturity of the product. All of this allows farmers to develop decent choices at every stage of the yield process.

     3. Soil Analysis and Optimization

Soil is a vital component of the farming environment. In order to get a healthy and rich crop field, soil inspection is an essential procedure in saving costs and conserving energy.

Some of the important soil characteristics to look for while leveraging specific plant species are soil temperature, water content, microbiome, and density of the soil. Evaporation rates and other crop-related processes may be studied using machine learning methods.

Micronutrients and macronutrients in the soil are prime components for crop health. We can anticipate the quality and quantity of the crop yield on the same parameters. Then, after the crops are in the ground, it’s crucial to keep track of their progress in order to maximize production efficiency. Understanding the relationships between crop development and the environment in which it will grow is critical for making modifications for better crop health.

Previously, human observations and analyses were used to evaluate soil quality and crop health. This strategy does not suffice as it’s not viable in a technology-driven world.
Thanks to AI, it is truly a marvel, we can now employ drones to collect aerial image data, which we can then teach computer vision models to use for smarter crop and soil monitoring.

     4. Breeding Species

Breeding different types of plants to grow in a specific habitat or to meet customer demand for a specific food preference is a time-consuming procedure. It includes studying consumer persona and deploying different mechanisms to detect genetic traits for plant mutation.

By examining current data regarding agricultural performance across diverse climates and ecosystems, as well as consumer purchase habits related to specific crops, deep learning algorithms can assist, accelerate and streamline the process. The findings may be required to create a probability model that can precisely forecast which genetic factors will result in specific crops desired by farmers as well as customers.

     5. Weed & Disease Detection and Prevention

Robust machine learning models and AI-powered systems can be used to identify and remove diseases and weeds that can drastically influence productivity and crop quality, all this can be done through eco–friendly methodologies.

For instance, AI-enabled robots can mechanically eradicate weeds, obviating the need to use pesticides by farmers. Robots can be taught to proactively recognize and destroy plants that exhibit indications of illness. Keeping robotics aside, environmental data may be utilized to maximize crop health and prevent illness from entering and spreading in the first place.

Image recognition solutions, primarily based on deep learning techniques, can actually help us in identifying plant illnesses and pests. Through image detection, classification, and image segmentation techniques, we can create models that can keep a keen eye on plant health.

     6. Enhancing Indoor Farming Environments

Various factors of the indoor farming environment, including climate, temperature, humidity, moisture, and sunshine, maybe optimized using AI. One can actively control the environment by continuous monitoring of plants through installed cameras and sensors.
The collected images are used as a dataset for neural networks and logic controllers. It eliminates guesswork when it comes to checking environmental conditions that are best for different crops. It allows farmers to create a more productive habitat.

     7. Irrigation Management

Excess of everything is bad. You cant randomly opt for excessive water utilization, which may generate adverse effects in the longer run.

Farmers may use AI and machine learning to figure out how much water to use for a certain crop, which varies depending on weather conditions, time of year, and soil qualities, among other factors. Water needs can fluctuate even in controlled greenhouse situations. AI systems aid in the detection of changes, allowing farmers to make necessary modifications for water irrigation systems.

     8. Mapping Yield with Demand

Today’s AI and machine learning technologies, which are at the heart of precision farming, go beyond forecasts based on previous data. They can now undertake multidimensional analysis bringing crop supply and demand closer together. Sensor and camera data, paired with computer vision technology, allows for more precise forecasting, reducing food waste.

Companies are using machine learning techniques to their advantage in Agri Industry

Let’s take a holistic view of some famous companies that have realized the importance of using AI in agriculture. The major aim is to improve crop yield and management, minimize cost and effort, and maximize ROI.

The future seems bright: according to Zion Market Research, the global market for AI in agriculture will reach $2 billion by 2024, growing at a CAGR of 21%.

Nature Sweet

Grower, packager, and supplier of commodities, NatureSweet LTD is based in San Antonio. The company is known for growing fresh and ripe tomatoes. They have installed cameras to monitor all sorts of budding problems in plants with the use of deep learning techniques.

“NatureSweet believes that machine learning analysis has increased harvest by 2 to 4%, with the founders hoping to eventually reach 20%.”

John Deere

Blue River Technology was acquired by John Deere, a thriving 180-year-old corporation recognized for its evocative green tractors. The food and agriculture chain, according to John Deere, is one of the most promising industries where machine learning may bring about tremendous shifts.

John Deere claims that machine learning will eliminate the need for pesticides by nearly 95% because computer vision will empower the agrarian economy to make decisions about every single crop in the field.

Machine learning will improve every aspect of agriculture, including crop harvesting, weather forecasting, soil tilling, unique area selection, fertilizers usage, and rainfall variability.

“We don’t have a lot of agricultural fields to bring back into production, but we’ll have to start making use of what we have,” says Ganesh Jayaram, Deere’s Vice President.

“Technology will account for 70% of agriculture, and we must do so in the most feasible eco-friendly way,” says Alex Purdy, Director of John Deere Labs.

Wrap Up

The role of Artificial intelligence in agriculture not only assists farmers in automating their agricultural operations but also results in precise cultivation for improved crop output and quality while using fewer resources.

Are you looking for ways to incorporate artificial intelligence into your farming operations? Let’s have a discussion about it. Our AI gurus are here to provide you with striking technology solutions for your specific problems.