Agriculture is how humans started to mold nature according to their needs. Humanity may have come a long way since then, changing the ways and methods of farming, but the basic objective remains the same; improving the quality of crops.
With the continuous increase in global population and land becoming scarcer, there is a need for farming to become more efficient with fewer resources. The introduction of various Artificial Intelligence and machine learning solutions is helping to provide much-needed innovation in this traditional business function.
It is now common for tractors, sprayers, or combines to steer automatically through the field and apply fertilizers only where required. The combine harvester today can measure real-time harvest yield, helping farmers understand the high yield and low yield areas of the field.
Farmers have more data and computing power available today than when we had while we put a man on the moon. AI can use the tremendous power of data and help the 5 trillion-dollar agriculture industry to produce healthier crops and make farming-related tasks easier.
Why AI solutions are needed in agriculture?
According to UN projections, the world's population is going to increase to 9.1 billion by 2050. Which means, approximately one third more mouths to feed with only 4% additional land that will come under cultivation! In order to meet this increasing demand, the agriculture sector needs to find more innovative approaches to face issues related to climate, soil, pests, etc.
AI is coming up as the agriculture industry’s most coveted technological evolution. AI-powered solutions will not only help farmers achieve a lot more with lesser effort but will also ensure faster production of crops with better quality.
It may still be an early stage, but here are some major ways how the AI transformation is taking place in agriculture:
Monitoring the health of soil and crops
Deforestation and degradation are critical problems being faced globally and is becoming a challenge for agriculture. AI algorithms can help in controlled farming methods that can improve the health of the soil. It can provide farmers with proper guidance related to planting, water management, nutrient management, crop rotation, etc.
Field images can be analyzed through computer vision, giving detailed reports regarding the current health of the soil, condition of leaves, or status of crops against molds and bacteria. This helps farmers to control the diseases timely using pest control methods.
One of the examples of the technology being used in the real world is a German-based startup PEAT that has developed an application called Plantix, which can identify various problems and nutrient deficiencies in the soil. The app is based on image recognition where a smartphone can be used to capture crop images and detect defects. The app also provides tips and solutions for soil restoration.
Another ML company Trace Genomics is providing soil analysis services for farming. It is helping farmers monitor their soil and crop health. This further helps in producing better-quality crops.
Machine learning and Computer vision solutions can be used by farmers to process data obtained by the software, to monitor the health of soil and crops.
Weed and pest detection
Weed competes aggressively with crops and causes fungi and bacteria to grow, which further decreases the harvest. Removing weed through herbicide is expensive and the best defense for a farmer is to prevent them from growing.
Automatic weed detection using AI is now possible. Machine learning can be used to manage weeds through computer vision. Data can be used to keep a check on weeds and control their spread. Farmers can use the information to spray chemicals and pesticides only where it is required. This avoids the need for spreading chemicals throughout the field. As a result, pesticide usage can be reduced.
Another big fear for most farmers is pests. They damage crops even before being harvested. Insects like locusts and grasshoppers are known for eating away the profits of farming. AI algorithms can detect insects landing on crops and farmers can use this information for the timely removal of pests.
An example of AI being used for pest monitoring is Nuru, an AI assistant that works with a smartphone to diagnose cassava disease in crops. The app also provides advice from experts in different languages.
Quality inspection of crops
AI makes it possible to replicate manual visual inspection of crops. It offers a non-destructive method of quality inspection, helping with the analysis of grain characteristics and grading. Computer vision can be used to understand images and recognize external characters of grains, further providing information on the quality to sort and grade the crops.
An example of machine learning being used to find defects in vegetables is Dlib, a machine learning library that supports image recognition capabilities, being used for defect pattern recognition on tomatoes. A similar ML model can be used on tomatoes by building a dataset of patterns such as them being infested with insects, having abrasions, or squashing due to mishandling.
The dataset can be used to create a knowledge base that can further be used to train machine learning algorithms to detect tomatoes with various patterns. This can help in sorting them out into different quality grades.
Defect pattern recognition on tomatoes. Source: RadioStudio
As we can see AI is not just a futuristic technology meant only for Silicon Valley. It can help improve the daily lives of farmers in the US, Mexico, Vietnam, or India since agriculture is a major part of the economy in these countries.
Skyl.ai offers machine learning solutions for real-time applications in agriculture, helping increase the effectiveness and profitability of farming.
Check out the various solutions that can be built using Sky Platform here.