The world's development goals cannot be met without food systems that are healthy, sustainable, and accessible to everyone. A predicted 9.7 billion people will need to be fed by 2050, and agricultural growth is the way to hit the goal.
Agriculture is essential to economic development, making up 4% of global GDP and more than 25% of GDP in some of the world's least developed countries.
Growth, poverty reduction, and food security that rely on agriculture are in jeopardy. But recent events like the aftermath of COVID-19, climate change, and pests are impacting the agriculture industry.
So, it is clear that the agriculture industry is waiting for ways to get out of a rut. But can AI help? To unfold the possibilities, keep on reading.
Why is the Agriculture industry lagging?
By default, the agriculture industry has some age-old issues we cannot fix.
Planting, harvesting, and processing crops are all examples of physical labor that the agricultural business has traditionally relied on. This dependence on human labor has slowed the industry's progress toward more mechanization. Farmers and workers have historically carried them out, and many have fought against the introduction of automated alternatives. But physical labor is expensive and time-consuming, often resulting in inefficiency and poor output.
No data tracking or analytics whatsoever
Agriculture is one sector that has fallen behind the times in terms of data tracking and analytics. Most farmers still do not record information on their crops, such as the state of the soil, the weather, or the harvest. Inefficiencies and lower yields result from a lack of data, which makes it harder to make educated decisions regarding planting and harvesting.
Lack of Technical Skills
Even when farmers keep track of data, it is not always consistent or reliable and, therefore, cannot be used to make informed decisions. This occurs because many farmers still rely on traditional methods to keep track of their information. This makes it hard to monitor data over time and make well-informed judgments. It's hard to use analytics for crop management if you don't have reliable data to work with.
How can AI help the Agriculture industry?
Better Decision making
Farmers have to make several choices every day, including what crops to grow, when to plant them, and how much water they need. They also have to decide when to harvest their crops and how to safeguard them from pests and illnesses. Farmers have unique challenges when making these decisions since they should consider various elements, including weather, soil, and historical data. All these decisions are purely based on intuition, not on expertise or data.
AI for soil health, pesticide measure management and more
Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is a major factor in determining the farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. But it has unique challenges. Most common methods to measure SHIs are in-lab wet chemistry. A few people also try spectroscopy based methods. But all of them require significant human input and effort, time-consuming, costly, and are low-throughput in nature. But by using UAV)-based multispectral sensing solution (UMS) now total Nitrogen can be used and measured swiftly.
Algorithms based on artificial intelligence also examine data about crops, such as their growth rate, yield, and nutritional content, and provide suggestions for improving agricultural practices.These suggestions are grounded in past data and can be adjusted for different crops and terrains. These suggestions are grounded in past data and can be adjusted for different crops and terrains.
By analyzing massive volumes of data, AI can aid farmers in making more informed decisions. Algorithms developed by artificial intelligence can examine meteorological data to forecast when crops will be favorable to grow crops or apply pesticides. Furthermore, AI can evaluate soil data to give insights into soil health and composition, assisting farmers in selecting optimal crop types and estimating water and fertilizer requirements.
Specific Directions for Every Farm
Different farms' soil compositions, climates, and other elements impact agricultural yields. That's why tailoring your farming practices to your specific land is essential. Artificial intelligence can help here by giving unique instructions for each farm.
Custom solution with AI
Coffee comes from the seeds of coffee cherries. To grow coffee plants, farmers need to take care of them by watering them, giving them nutrients, and ensuring they are healthy. This cannot be easy because there are many plants to care for, and it takes a lot of time.
A group of researchers used a type of machine learning called YOLO to help them monitor coffee plants. YOLO ((You Only Look Once) which works on the basis of Convolutional Neural Network (CNN). They took pictures of the plants and used YOLO to analyze the images. YOLO could tell them how healthy the plants were and when they were ready to be harvested. This made it easier for farmers to care for their plants and get more coffee beans.
They also created CoffeApp that farmers can use on their phones to help them monitor their plants. The app can tell them when the plants are ready to be harvested and how much coffee they can expect to get. This is important because it helps farmers save time and money while ensuring they get the best coffee possible.
Forecast Crop Yield Production
Crop yield forecasting is important for regional and global food security. Forecasting year-to-year variations in the yields of major crops at the regional and national levels can strengthen the ability of societies to respond to food production shocks and food price spikes triggered by extreme events. Within-season crop forecasting is also essential for farmers to make more informed crop management and financial decisions.
Many approaches have been used to forecast yield at the regional and field levels, such as field surveys, mathematical models that simulate crop development and yield, statistical models, remote sensing, and combinations of surveys, models, and remote sensing. However, crop yield forecasting is challenging due to the many factors involved, such as crop and variety, soil type, management practices, pests and diseases, and climate and weather patterns during the season.
Using AI and ML for Crop Yield Production
Machine learning (ML) is an application of AI that gives computers the ability to learn without being explicitly programmed. It consists of various mathematical algorithms that make learning possible.
For example, researchers at the University of Florida used machine learning algorithms to predict crop yields for different crops such as strawberries and wheat. They used high-resolution aerial orthoimages and hyperspectral imaging collected by sensors mounted on unmanned aerial vehicles (UAVs) to predict strawberry yield. They also integrated satellite and climate data to predict wheat yield in Australia using machine learning approaches.
Stop Crop Disease with Computer Vision
Fungi, bacteria, viruses, and pests are only some of the potential culprits in the spread of crop diseases. The effects of these pathogens on crops can be severe, including decreased yields, worse quality products, and even crop collapse. Early detection of agricultural illnesses is essential for containing the disease and limiting its harm.
When looking for symptoms of illness in their crops, farmers have historically depended only on visual inspection. This procedure, while effective, can be laborious and may not pick up on mild illnesses until they've spread.
Application of Drones and UVAs
Here is when AI comes in handy. Artificial intelligence can examine photos of crops for symptoms of disease using computer vision technologies.
With the help of AI, unmanned aerial vehicles (UAVs) can be used to monitor crops for signs of disease and deliver targeted sprays of pesticide. Cameras on board the UAVs provide bird's-eye views.
Edge computing AI algorithms analyze these images to establish the severity of the infestation, the types of crops at risk, the best strategy for preventing the spread of the illness, and the optimal dosage of pesticides. The technique has great promise for boosting agricultural productivity and income.
Once again, drones can employ variable spraying technologies instead of insecticides equally as the whole plant is not sick. A particular part of it needs this care. This can lower the number of pesticides that must be sprayed in conformity with international regulations.
The adoption of this approach requires an understanding of the outbreak of disease. This can be accomplished with UVAs. As a result, variable spraying technology-based drones are needed as an alternative to traditional methods of spraying pesticides uniformly. In this way, farmers can also reduce pesticide usage following international standards.
You can learn more about this in this research paper.
Incorporating AI into farming does not necessitate costly hardware or software purchases on the part of farmers. Instead, the farmer can use various AI programs on mobile devices that are both cheap and common. These smartphone apps let farmers enter information about their soil, crops, and weather to get analysis and advice from AI systems.
In addition, adopting AI can give farmers data-driven insights to make better decisions than conventional farming practices, where farmers rely on their expertise and intuition. Advice on when to sow crops, how much fertilizer to use, and when to apply pesticides. All can be the result of these analyses. Farmers may maximize their return on investment by making use of these resources, boosting their output and income.
Moreover, the investment required to reap the benefits of AI in agriculture is modest. Artificial intelligence (AI) can help farmers save money long-term by reducing risks, increasing agricultural yields, and decreasing waste. Small farmers who may not have the financial means to invest in costly farming equipment and technologies can take advantage of the benefits of AI farming because of its low cost.
For example, CropRecords is an app that allows creating and tracking tasks for spraying, seeding, fieldwork, and harvesting. Farmers have to share their data. The backend system synchronizes all the files between users and software platforms in the SWAT MAPS brand. The app is available for Android, iPad, and iPhone. So it gets easy, and they do not have to rely on others.
No doubt, AI has the potential to redefine the agriculture industry and improve the global scenario. But adopting AI can be a significant roadblock if an efficient company is not helping you. Ionio.io allows you to overcome hazards and integrate AI into your workflow smoothly.