AI in Agriculture

AI technology is founded on the idea that human intelligence may be described in such a way that a computer can simply imitate it and carry out tasks ranging from easy to sophisticated.

According to Markets&Markets, spending on AI technologies and solutions in agriculture will increase from $1 billion in 2020 to $4 billion in 2026, representing a 25.5 percent compound annual growth rate.

Consider having at least 40 critical procedures to thrive and monitor at the same time throughout a big farming region, which may be hundreds of acres in size.

Diving deeper into how climate, seasonal sunlight, animal, bird, and insect migration patterns, crop use of specific fertilizers and insecticides, planting cycles, and irrigation cycles all affect production is a wonderful problem for machine learning. Excellent data has never been more important in determining the financial success of a crop cycle. That’s why farmers and agricultural development firms are doubling down on data-driven strategies and increasing the size and scope of how they utilize AI and ML to boost agricultural yields and quality.

Why is it so difficult for farmers to use AI?

Farmers have a tendency to think of artificial intelligence (AI) as something that only works in the digital realm. This isn’t due to apprehension about the unknown or a desire to be conservative. Their apprehension derives from a lack of understanding of how AI can be used in the real world.

Because AgriTech vendors fail to adequately explain why their solutions are valuable and how they should be deployed, new technologies sometimes appear complex and unduly expensive. This is what occurs in agriculture when artificial intelligence is used. Although AI can be beneficial, technology companies must still do a lot of work to assist farmers in properly implementing it.

Farmers’ Challenges When Adopting Traditional Farming Methods

The following is a list of some of the most common agricultural issues.

Climate variables such as rainfall, temperature, and humidity all play an influence in the agriculture lifecycle. Climate change is a result of increasing deforestation and pollution, making it difficult for farmers to make judgments about how to prepare the soil, sow seeds, and harvest.

Every crop necessitates a certain type of soil nourishment. In soil, three primary nutrients are required: nitrogen (N), phosphorus (P), and potassium (K). Nutrient insufficiency can cause crops to be of poor quality.

Weed control is critical in agriculture, as evidenced by the agriculture lifecycle. If not controlled, it can lead to an increase in production costs as well as the absorption of nutrients from the soil, resulting in a nutrient deficit.

Agriculture’s Life Cycle

The agricultural process can be divided into several parts:

Soil preparation: This is the first stage of farming when farmers prepare the soil for seed sowing. Large dirt clumps are broken up and debris such as sticks, rocks, and roots are removed. Also, depending on the type of crop, add fertilizers and organic matter to produce an optimum environment for crops.

Seed sowing: This stage necessitates consideration of the spacing between two seeds as well as the depth at which seeds should be planted. Climate conditions like temperature, humidity, and rainfall are critical during this stage.

Adding Fertilizers: Maintaining soil fertility is critical for a farmer’s ability to continue growing nutritious and healthy crops. Fertilizers are used by farmers because they contain plant nutrients including nitrogen, phosphorus, and potassium. Fertilizers are simply nutrients that are placed in agricultural fields to augment the elements that are already present in the soil. This stage also determines the crop’s quality.

Irrigation: During this stage, the soil is kept moist and the humidity is maintained. Crop growth can be hampered by underwatering or overwatering, and if not done correctly, can result in crop damage.

Weed control: Weeds are unwelcome plants that grow near crops or along farm boundaries. Weed control is crucial to consider because weeds reduce yields, raise production costs, obstruct harvest, and affect crop quality.

Harvesting is the act of collecting mature crops from the fields. This task necessitates a large number of laborers, making it a labor-intensive activity. Cleaning, sorting, packing, and refrigeration are all part of the post-harvest process.

Storage: This phase of the post-harvest system is when the products are preserved in a way that ensures food security outside of agricultural seasons. Crop packing and transportation are also included.

Role of AI In Agriculture

1. Monitoring Of Crops And Soil

Let’s start at the beginning.

Micronutrients and macronutrients in the soil are important for crop growth and yield quantity and quality.
Then, after the crops are in the ground, it’s crucial to keep track of their progress to maximize production efficiency. Understanding the relationships between crop growth and the environment is critical for making adjustments for better crop health.

The kind of soil and the nutrients in the soil influence the type of crop planted and the quality of the crop. Soil quality is deteriorating as a result of increased deforestation, making it difficult to identify the condition of the soil.
Previously, human observation and judgment were used to determine soil quality and crop health. This strategy, however, is neither precise nor timely.

A tech start-up established in Germany PEAT has created Plantix, an AI-based application that can detect nutrient deficits in soil, as well as plant pests and diseases, and give farmers advice on how to utilize fertilizer to increase harvest quality. Image recognition tech is used in this app. Farmers can picture plants with their smartphones. This application includes short films that show soil restoration processes, as well as tips and alternative options.

2. Keeping Track Of Crop Maturity

In agriculture, manual observation of crop growth phases is an example of a labor-intensive operation that AI may assist with.

Researchers were able to accomplish this by collecting photos of wheat at various “heading” stages over two years and in various lighting conditions, allowing them to develop a “two-step coarse-to-fine wheat ear recognition method.”

This computer vision model then outperformed human observation in properly recognizing wheat growth stages, removing the need for farmers to visit their fields regularly to inspect their grain.
Imagine having to manually verify the ripeness of tomatoes on a large scale. Well..

That, too, is something AI can assist with!

3. Detection Of Insects And Plant Diseases

We’ve already seen how AI computer vision can identify and analyze crop growth and soil condition, but what about less predictable agricultural conditions?

Artificial intelligence systems examine satellite images and compare them to past data to identify whether any bugs have landed and, if so, which species. It then sends messages to farmers’ phones, allowing them to take the required precautions and implement strategies, allowing AI to aid in their pest management operations.

4. Produce Grading And Sorting

Finally, even after the crops have been harvested, AI computer vision can continue to assist farmers.

Imaging algorithms can select “good” produce from the flawed or just plain unattractive, much as they can discover faults, diseases, and pests while the plants are growing.

Computer vision can automate the sorting and grading process by evaluating fruit and vegetables for size, shape, color, and volume, with accuracy and speed much exceeding that of even a skilled professional.

5. Aerial Survey And Imaging

It’s probably unsurprising at this time that computer vision has some fantastic applications for surveying land and monitoring crops and livestock.

Aerial imaging can be used to spot cattle.

That isn’t to say it isn’t important for smart farming.

Farmers may use AI to evaluate imagery from drones and satellites to monitor crops and livestock. In this manner, they may be warned right away if something appears to be wrong without having to constantly monitor the fields.

Aerial imagery can also help improve pesticide spraying precision and efficiency. As previously said, ensuring that pesticides only travel where they’re supposed to saves both money and the environment.

6. Precision Farming and Predictive

Agriculture AI applications have produced apps and tools that assist farmers in performing correct and regulated farming by offering proper advice on irrigation systems, crop rotation, prompt harvesting, pest attacks, etc.

AI-enabled technology solutions forecast weather changes, analyze crop stability, and assess farms for the existence of pest problems, as well as poor plant nutrition, using parameters such as temperature, wind speed, and solar radiation, using ML algorithms in conjunction with images captured by satellites and drones.

Drone-based Ariel imaging systems for crop health monitoring have been introduced by SkySqurrel Technologies. The drone gathers information from fields in this fashion, which is then delivered to a pc by USB drive and reviewed by experts.

7. Irrigation System That Is Fully Automated

In the automated irrigation system, we can also employ weather prediction. What do you mean by that? It sounds like an interesting use case to have a system that tells you the soil is dry but you don’t need to water because it will rain in a few hours.

Every country in the world is today compelled to use water in an extremely efficient manner.

According to recent studies, water is becoming increasingly scarce around the world, with more than a third of the world’s population facing total water scarcity by 2025.

Water shortage is a serious issue for farmers in agriculture as well, thus one of the irrigation techniques used to improve water usage is drip irrigation, which is deployed as an automated irrigation system for small-scale farms.

Another irrigation system that uses weather prediction is an automated irrigation system.

Do you have any questions about data science applications in agriculture? Leave your thoughts in the comments section.

8. Forecasting Of The Weather

For farmers to determine the right time for sowing seed is very difficult but now they can analyze weather patterns via using weather models, which helps them schedule the kinds of crops that can be developed and when seeds must be sown, with the advent of AI.

9. Livestock Health Monitoring

We’ve mostly talked about plants thus far, but agriculture is much more than wheat, lemons, and oranges.

Animals, like plants, are an important part of our agricultural systems, and they require a little more tracking than plants. Is computer vision capable of keeping up with moving buffalos, chickens, and pigs?

CattleEye’s training data enables livestock to be tracked and annotated using bounding boxes and key points. Using V7, cows were marked with bounding boxes and important points.

V7 CattleEye’s annotation of cows with bounding boxes and critical points is a superb example of an AI-first startup in the agriculture market. They monitor cow health and behavior with overhead cameras and computer vision techniques.

10. Automatic Weeding

The use of AI in weed… well, weeding isn’t limited to intelligent sprayers. Other computer vision robots are adopting a more direct approach to removing invasive plants.

Now, recognizing a weed in the same manner that computer vision can detect a bug or an unusually acting chicken doesn’t save the farmer any time. To be of even more assistance, the AI must both locate and eliminate the weed.

Physical weed removal not only saves the farmer time and effort but also decreases the need for chemicals, making the entire agricultural process more ecologically friendly and sustainable.

11. Climate Change Adaptation

Climate change is a looming threat that has already had an impact on agriculture. Experts in data science, on the other hand, are working hard to find strategies to adjust to the change.

One experiment involves providing IoT sensors to rice farmers in Taiwan so that they may collect vital information about their harvests. Even if climatic changes make it difficult, they will assist farmers in optimizing their output cycles.

Due to catastrophic climate change, the traditional farming calendar is no longer sufficient, but data analysis can alter the future of farming.

Agriculture soil data is also being analyzed by data scientists to learn how soil can cope with climate change by releasing greenhouse gases, as well as how soil can adapt to climate change.

12. Intelligent Spraying

We’ve seen that computer vision is effective at detecting agricultural problems, but it can also aid in their prevention.

Pesticides and fertilizer can be sprayed uniformly throughout a field using UAVs equipped with computer vision AI.

Due to real-time recognition of target spraying areas, UAV sprayers may operate with extreme precision, both with respect to the size and volume to be sprayed. This minimizes the danger of contamination of crops, humans, animals, and water supplies dramatically.

13. Robotics in Agriculture

Robots that can easily do several duties in farming fields are being developed by AI businesses. When compared to people, this type of robot is trained to suppress weeds and harvest crops at a faster rate with bigger volumes.

While picking and packing crops, these robots are programmed to assess crop quality and detect weeds. It’s also capable of dealing with the challenges that agricultural labor experiences.

14. Produce Sorting And Grading

Finally, even after the crops have been harvested, AI computer vision can assist farmers.

Imaging algorithms can select “excellent” products from the flawed or just plain ugly, much as they can discover faults, diseases, and insects as the plants grow.

Computer vision can automate the process by evaluating fruit and vegetables for size, shape, color, and volume, with accuracy and speed much above that of a skilled professional.

DATOS Project Case Study

According to recent press reports, the DATOS project used artificial intelligence, machine learning, and other data science approaches to analyze data collected remotely. It also used systems to provide geospatial outputs that might be used for catastrophes, agriculture, and other applications.

The DATOS Project has devised a method for mapping outcrops using satellite photos and extracting the temporal signature of crops from radar satellite imagery.

Furthermore, DATOS creates flood status maps by obtaining satellite pictures and allowing AI to identify flooded areas.

In the event of severe weather, it can detect flooding in areas impacted by significant rainfall and relay these mapped out locations to the appropriate DOST regional offices.

Real-World Examples of AI in Agriculture

Farmers in Egypt, for example, utilize water pumps to gather water from the Nile River and use it to irrigate their crops. Irrigation of huge fields is done with advanced water sprinklers, which ensures that the crops get enough water to flourish. This strategy can help you save water by allowing you to use it more efficiently.

Farmers can manage their grass more effectively with the use of mobile apps like “FamGraze,” which recommends the cheapest feed for their animals. There is no need for paper or spreadsheets, and it saves time. This enables you to work more quickly and accurately both on and off the field!

Farmers can readily transport fertilizers or other farm goods to other parts of the world thanks to modern transportation technology. Consumers in Dubai, for example, will receive fresh carrots from Africa on the same day that the carrots leave the African garden.

Application of AI in Agriculture

1. Farmers can analyze weather conditions by using weather forecasting, which helps them plan the type of crop that can be grown and when seeds should be sown, with the help of Artificial Intelligence. With the change in climatic conditions and increasing pollution, it’s difficult for farmers to determine the right time for sowing seed.

2. Monitoring system for soil and crop health: The kind of soil and the nutrients in the soil influence the type of crop planted and the quality of the crop. Soil quality is deteriorating as a result of increased deforestation, making it difficult to identify the condition of the soil.

A tech start-up established in Germany PEAT has created Plantix, an AI-based application that can detect nutrient deficits in soil, as well as plant pests and diseases, and give farmers advice on how to utilize fertilizer to increase harvest quality. Image recognition technology is used in this app. Smartphones can be used by the farmer to photograph plants. Short movies on this application show soil restoration procedures, as well as recommendations and other solutions.

Trace Genomics, meanwhile, is a machine learning-based startup that assists farmers with soil analyses. Farmers can use such an app to track the quality of their soil and crops, resulting in healthier, more productive crops.

3. Drones are used to monitor soil quality and analyze crop health. Drone-based Ariel imaging solutions for crop health monitoring have been introduced by SkySqurrel Technologies. In this method, the drone collects data from fields, which is subsequently sent to a computer via USB drive and examined by experts.

This company analyses the taken photographs with algorithms and provides a full report on the farm’s current health. It assists farmers in identifying pests and bacteria, allowing them to utilize pest management and other ways in a timely manner.

4. Precision Farming and Predictive Analytics: AI applications in agriculture have developed applications and tools that assist farmers in performing accurate and controlled farming by providing proper guidance on water management, crop rotation, timely harvesting, type of crop to be grown, optimum planting, pest attacks, and nutrition management.

AI-enabled technologies predict weather conditions, analyze crop sustainability, and evaluate farms for the presence of diseases or pests, as well as poor plant nutrition, using data such as temperature, precipitation, wind speed, and solar radiation in conjunction with machine learning algorithms and images captured by satellites and drones.

Farmers who don’t have access to the internet can profit from AI right now using basic technologies like an SMS-enabled phone and the Sowing App. Meanwhile, farmers with Wi-Fi connectivity can use AI programs to acquire an AI-customized plan for their farms on a continuous basis. Farmers can fulfill the world’s growing food demand with IoT and AI-driven solutions that grow productivity and revenue without depleting scarce natural resources.

AI will help farmers transform into agricultural technologists in the future, using data to maximize yields down to individual plant rows.

5. Agricultural Robotics: AI firms are working on robots that can do a variety of activities in farming fields. When compared to people, this type of robot is trained to suppress weeds and harvest crops at a faster rate with bigger volumes. These robots are programmed to inspect crop quality and detect weeds while picking and packing crops at the same time. These robots can also deal with the difficulties that agricultural labor faces.

6. A system that uses artificial intelligence to detect pests: Pests are one of the most destructive enemies of farmers’ crops. AI systems analyze satellite photographs and compare them to previous data to determine whether any insects have landed and, if so, what species of insect has landed (locust, grasshopper, etc.). And send notifications to farmers’ smartphones so that they can take the necessary measures and employ the necessary pest management, allowing AI to assist farmers in their pest control efforts.

Conclusion

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

Companies that improve machine learning or Artificial Intelligence-based products or services, such as training data for agriculture, drones, and automated machine manufacturing, will benefit from technological advancements in the future, which will help the world deal with food production problems for a growing population.