Harvesting the Future with AI
It’s a no-brainer that the rising population puts increasing pressure on livestock and agriculture. The UN estimates an increase in population rate, from 7.5 billion to 9.7 billion by 2050. However, on the agriculture front, only a marginal expansion of 4% of cultivatable agricultural land will be added to the sector by 2050. The disparity calls forth challenges that can only be addressed by modern farming practices.
Despite having a short history, Artificial intelligence has proved to be a capable partner in solving a few of the intricate and complex problems we are facing today. The $5 trillion agribusiness is no different.
One out of four workers across the globe is employed in the agriculture sector, yet merely 400 million small-scale farmers live in poverty. Despite being a key contributor to the agro-food supply chain, small-scale farmers continuously struggle to compete with large producers due to a lack of machinery, scant farming resources, and oblivion towards modern farming practices.
Leverage AI to Do More With Less
The increasing demand for agricultural products, when weighted up against the marginal expansion of cultivatable agricultural land is a clear indication that farmers should look beyond legacy farming practices to adapt and evolve. Farmers should embrace modern farming technologies and robust artificial intelligence frameworks to improve productivity, maximize yield, and increase crop production
In an ecosystem wherein the resources are limited, we should embrace opportunities that enable us to “Do More With Less”.
Integrating AI into Traditional Agriculture
For centuries farmers have toiled against nature to meet the world’s demands. But, when the supply drops and the demand for agri-products rises, agribusinesses are forced to look into more contemporary techniques;
- Develop more cultivable land (At present, nearly 37.7% of the total land surface is used for crop production.)
- Find means and measures to increase the productivity of crops within available land
Multiple variables (like climate, irrigation, soil fertility, etc) play a crucial role in delivering sustainable production conditions. These variables can have complex integration across the value chain, and overlooking a single variable can significantly impact crop production.
Data is generated at every point, and data forms the baseline for modern farming.
Data-driven smart farming practices can be introduced across the agriculture sector to optimize cost and productivity. Data from multiple agri-food sectors needs to be integrated, facilitating a seamless flow of information.
Collaborative intelligence, artificial intelligence when collectively employed with years of agriculture experience can be a key enabler that allows agrifarms to make data-centric decisions and help achieve objectives that are beyond the reach of present-day challenges.
Artificial Intelligence is a transformative force poised to revolutionize traditional farming practices. Precision farming is one of the most practiced applications of artificial intelligence in agrifarming. Precision agriculture (precision farming), is an approach that utilizes modern technologies to optimize various aspects of the agricultural process. It involves the use of advanced technologies such as drones, GPS guidance systems, sensors, robotics, and data analytics to enhance decision-making and improve the efficiency of farming operations.
AI can bring revolutionary changes in agribusiness. By using big data, IoT sensors for capturing and analyzing data, and integrating cognitive automation, businesses can reduce a considerable amount of manual work to optimize the agriculture lifecycle.
Challenges Faced by Farmers in Traditional Farming
The world is changing. Flash news of floods, hurricanes, and heat waves tops the headlines. With increasing climatic change, the world is taking continuous leaps and bounces. It has become increasingly challenging for traditional agribusinesses to keep up with the uncertainties.
- Limited land size poses a significant challenge, impacting economies of scale
- Lack of access to reliable irrigation systems as an aftermath of water scarcity
- Access to quality data, mainly in remote areas, remains a barrier
- Struggle to compete with larger, more mechanized farms
- Uncertainties of climate change, traditional farming is highly dependent on weather patterns
- Extensive time delays in detecting and implementing pest control measures
- Limited access to real-time information
- Expensive labor costs
- Limited crop diversity
- Limited access to markets
- Challenges in effectively managing crop diseases
- Inadequate transportation and storage facilities contribute to post-harvest losses
What’s the Future of AI in Agriculture?
Artificial intelligence in agriculture can help gather accurate data about insect pest infestations, explore soil health, implement proven irrigation practices, monitor weather conditions, and make data-driven recommendations on the application of fertilizer and pesticides.
By shifting towards an artificial intelligence-enabled agri-ecosystem, businesses will be able to save the excess use of water, efficiently utilize manpower, put forth a data-driven approach in applying pesticides, integrate automated weeding techniques, ensure adequate soil fertility, and increase crop production.
AI in Action
Seeds of Precision: Maximizing Output with Seed Selection
Selecting the right seed plays a pivotal role in determining the crop yield. Agribusinesses should consider multiple variables (eg: soil type, climate, irrigation, etc) when selecting seeds for the farm. Smart farms are introducing machine learning algorithms to select seeds that are high-yielding, resistant to pests, drought tolerant, disease resistant, and have the potential to overcome the risks associated with traditional farming practices.
Seed companies can ensure that they sell the best variety of seeds specific to the geographic conditions and the topography of the farmland. This gives farmers an option to select the best-suited seeds considering climate changes, reduces the risk of low crop yield, and grows healthy crops.
Safeguarding Crops: Using AI to Identify Anomalies in Crop Production
Machine learning algorithms are trained with huge data sets to detect anomalies at the early stages. By leveraging artificial intelligence, agri producers can train ML models to identify defects, deviations, or abnormalities in the quality or characteristics of fruits and vegetables produced on the farm.
For eg: Artificial intelligence (Image recognition, deep learning algorithms, machine learning, etc) techniques can be used to compare the size, shape, color, and other biological characteristics of products produced within a farm. The data can then be used in real-time to make smart data-driven decisions. Like, how cognitive robotic systems can identify early disease detection in plants and take precautionary measures, allowing targeted and localized treatments without causing fatal delays.
Early crop disease detection allows farmers an opportunity to execute a timely and personalized treatment plan to protect plant health and ensure sustainable farming.
Autonomous Tractors: Driving Productivity in Farming
Self-driving, autonomous vehicles are a modern-day marvel. Be it a fully autonomous tractor or a tractor with an Advanced Driver Assistant System (ADAS) is equipped with various cutting-edge technologies like GPS, sensor technology, and 360° vision cameras to intelligently operate within the limits of its working environment.
Autonomous tractors are a powerful enabler in precision farming. The tractors offer accuracy in farm operations, increase efficiency, and reduce manpower usage. These machines can precisely plant seeds, spray pesticides, apply fertilizers, reduce environmental impact, and enable faster time to market. The agricultural sector must prioritize innovation that improves operational efficiencies, and autonomous tractors are a step towards future-proofing farming operations.
Put Every Drop to Work – Optimize & Reduce Water Footprint
Water, the elixir of life, is facing critical challenges due to devastating climate changes. Modern-day agribusinesses should invest in AI technologies to efficiently manage the finite resource.
In the US alone, per day, nearly 4.5 billion gallons of water is wasted due to overwatering. Bringing in technologies like IoT can aid businesses in laying out a systemic approach towards farm irrigation. IoT sensors can capture the real-time moisture condition of soil, once the data is passed to an AI system, it can determine the amount of water required to ensure the healthy growth of crops.
Agribusinesses that practice precision agriculture have invested in smart irrigation systems to analyze site-specific variables like, moisture in the soil, weather conditions, rainfall, etc to automate the whole irrigation process and limit labor-intensive interventions.
According to PwC, IoT-enabled Agricultural (IoTAg) monitoring is a smart approach, and the connected segment is projected to reach $4.5 billion by 2025.
Digital Surveillance to Secure Yields
When comes to surveillance, AI has proven to be a steadfast partner. Advanced video analytics clubbed with AI and ML algorithms are used to mull through video feeds in real time and identify potential breaches.
AI-driven digital surveillance in agribusinesses have
- proven effective in securing remote facilities
- reduce domestic and wild animals’ potentially entering into agricultural lands and destroying crops
- dissuade trespassers from entering agricultural lands
- Reduce theft and keep farm equipment safe
- monitor resource movement across huge farmlands
- restrict break-ins in remote farmlands
Yield Mapping in Precision Agriculture
Yield mapping is a technology-driven approach to identifying areas in agricultural land with potential possibilities for higher or lower crop yields. With the help of artificial intelligence, agronomics can map possible agricultural plots based on soil color and other parameters to predict the potential soil yields for a given crop.
Yield maps have been helpful for large-scale agricultural landowners to identify areas of land where the productivity is less. The data can be used to identify the heterogeneity within a field, make informed decisions on what crops are ideal for the agricultural land, conduct further studies on what fertilizers and pesticides need to be used, does the land mass requires additional irrigation, etc.
Intelligent Spraying & Automatic Weeding
Efficient pest control is crucial, and technology has opened up new ways to administer pesticides. The traditional practice of uniform spraying can lead to considerable environmental hazards. Cognitive robots can be introduced to identify pests and pathogens at the earliest so that a localized treatment plan can be administered. This environment-friendly approach enables farmers to minimize pesticide use without sacrificing crop yields.
Traditional agriculture practices are labor-intensive and time-consuming. When the race for survival starts there is a rational competition between the weeds and the crops. Sharing the limited resources (fertilizers, water, and minerals) can put enormous strains on crop growth. Over 95% of herbicides sprayed by farmers do not reach the intended locations because they are sprayed or spread everywhere in the agricultural fields. Automatic weeding machines utilize one of several means to take a targetted approach to kill weeds including mechanical, flame, or herbicidal spray.
According to stats published on Freethink, a farming robot can kill 200,000 weeds per hour with lasers. Which sounds like a striking number.
Produce Grading and Sorting
The application of AI in agriculture need not be limited to plant growth, cost optimization, and yield augmentation. Artificial intelligence can also be used in harvesting. While harvesting, cognitive systems can be used to inspect fruits and vegetables to detect spots, defects, and irregularities, and sort them based on the predefined criteria. The same technology can also used to grade fruits and vegetables produced within an agrifarm.
The Promising Horizon of AI In Agriculture
The horizon appears bright as we continue to innovate and develop smarter, more efficient, and more sustainable ways of farming.
Technological advancements in agriculture will help the world deal with hunger and food insecurities. Artificial intelligence can open up new possibilities to combat the challenges affecting the world’s food supply system.
