Ever since humans discovered a farming and moved from a nomadic hunter-gatherer type lifestyle to one of a farmer, our demands for food grains have increased unabated. As populations have surged globally, experts at the UN Food and Agricultural Association (FAO) estimate that an additional 2 billion mouths need to be fed by the year 2055.
This means that the agricultural industry needs to massively scale up in the coming years. With the acreage under cultivation slated to improve by just under 4%, the pressure is intense. It is no longer a game of just planting more crops or breeding additional cattle.
Farm efficiency needs to be nearly doubled for us to meet our targets. Farming, however, is dependent upon the forces of nature for most of its products and with the uncertainty of rain, lack of available workers and the growing need for a better yield year after year has put farmers and farms under intense pressure.
To counter this need and reduce dependency on sheer luck for good yields, agricultural scientists have started turning to Artificial Intelligence both in the terms of agricultural products and in-field farming methodologies.
One facet of computing, in particular, is all set to become the most disruptive technology in its space as it can learn, understand and respond to a magnitude of situations to increase farming efficiencies – Cognitive computing.
This has given rise to terms like digital agriculture, where technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery, and advanced analytics are empowering small-holder farmers to increase their income through increased crop yield and greater price control.
Microsoft is currently working with around 175 farmers in Andhra Pradesh, India in alliance with ICRISAT to provide advisory services for sowing, land, fertilizer and so on. This initiative has already resulted in a 30% higher yield per hectare on average compared to last year.
AI has slowly made inroads into rural India, replacing traditional farmer wisdom with inputs from cognitive computing and machine learning to increase output to levels not seen since the Green Revolution. To facilitate this, Microsoft used historic climatic data from the last 30 years and analyzed it using AI.
This helped in determining the Moisture Adequacy Index, which is the standard measure for ascertaining the degree of adequacy of rainfall and moisture in the soil to meet potential moisture requirements for crops.
Coupled with the Pest Risk Prediction API that leverages AI and machine learning to indicate the risk of pest attacks in advance, farmers have been able to reduce their dependency on agrochemicals and increase yields.
Drones
Drones have become the farmer’s newest high-tech friend. Providing in-depth field analysis, spraying crops over long distances and monitoring crops with extremely high-efficiency rates, drone technology is rapidly becoming an indispensable tool for farmers.
Continuous deforestation and lowered soil quality due to chemical agent overuse are a major threat to food production. Soil erosion alone can cost the industry around 40 billion dollars annually. Vineyards are particularly susceptible to climatic and soil quality changes and yields can differ significantly over the years.
Drone technology by companies like SkySquirrel Technologies aims to help farmers improve their crop yield and monitor crop health. SkySquirrel uses specialized algorithms to integrate and analyze captured images and data to provide a detailed report on the health of the vineyard, specifically the condition of grape leaves.
Since grape leaves are often telling tales for diseases (such as molds and bacteria), reading the “health” of the leaves is often a good proxy for understanding the health of the plants and their fruit as a whole.
Sustainable Farming
Farming was traditionally a process that allowed the land to rejuvenate. However, with increased demand for food, farmers often resort to “soil abuse” to increase yield. This increased yields initially, but over time, degraded the quality of land to a point where the yield was too low to even pay for the cost of seeds.
Services like FarmShots are focused on analyzing agricultural data from satellites and drones using AI to detect diseases, pests and poor plant nutrition on farms. The software can monitor and limit fertilizer usage by up to 40 percent.
Irrigation
Farming alone consumes about 70% of available freshwater around the globe. As irrigation is a human-intensive process, automation techniques can leverage AI and machine learning to analyze historical weather patterns, soil quality, and crop type to help farmers better manage their water resources.
Using Cognitive IOT solutions, water management can be bettered by sowing the right crop at the right time, reducing water wastage and increasing yields.
Precision Farming
Being at the right place, at the right time with the right product is true for all businesses, however, for farming, it is the sole mantra for success. Precision farming uses AI, machine learning and remote sensing to help reduce and replace repetitive and labor-intensive aspects of farming.
With increased assistance and guidance regarding crop rotation, harvesting, planting, pest control, and water management, precision farming can help increase yield and farm profits while reducing wastage to the maximum extent possible.
Weeds
Weeds are a problem that can just not be addressed using agrochemicals. Weeds cause losses in excess of over $40 billion to farmers in the USA. Technologies like See & Spray by Blue River Technology leverage computer vision to monitor and spray weeds on cotton plants with precision, helping to prevent herbicide resistance.
This technology reduces the volume of chemicals used by over 80% and expenditures on herbicides by up to 90%. With a billion pounds of pesticides being used every year in the US alone, the results look very promising.
Labour
Another major problem that plagues agriculturists is labour. With the industry projecting a gradual decline in agricultural workers by 2024, harvesting and packaging for distribution are likely to be hit hard. Companies like Harvest CROO Robotics have developed a robot to help strawberry farmers pick and pack their crops.
With the capability to harvest over 8 acres in a single day, this robot can potentially match what 30 labourers could do in a day! With labor wages, salaries and contract expenses accounting for over 40 percent of farm costs, this machine can improve farmer margins and help reduce procurement and shipping costs.
Challenges in AI adoption
Although Artificial Intelligence offers a great opportunity for application in agriculture, there is still a lack of familiarity with high tech machine learning solutions in farms across most parts of the world.
Farmer illiteracy and the unavailability of mobile networks can reduce the capability of farmers to adopt such technologies. Exposure of farming to external factors like weather conditions, soil conditions and the presence of pests is quite a lot.
So what might look like a good solution while planning during the start of harvesting, may not be an optimal one because of changes in external parameters.
As innovations in farming technology continue to occur globally, the industry is still at a very nascent stage. AI-driven technologies are gearing up to help improve efficiency and to address challenges facing the agriculture industry including crop yield, soil health, and herbicide-resistance.
Agricultural robots are poised to become a highly valued application of AI in this sector. The scope of AI in agriculture is enormous. As the prices of such innovative technologies reduce and are available in the hands of marginal farmers, estimates are that we just might be able to reach the magic number needed to feed our ever-growing population while sustaining the quality of land and improving yields.
