AI Usage in Agriculture is Addressing Food Insecurity
Artificial Intelligence (AI) refers to computer systems that can perform tasks that would normally require humans, including visual perception, speech recognition, decision-making and language translation. AI development has exploded within the last several years, and industries are beginning to adopt such systems to increase productivity and address challenges to growth.
The agricultural sector is one industry that is benefitting from the implementation of AI technology, and people are discussing and enforcing new applications for this technology every day. Several companies, such as IBM, FAO and Microsoft, are developing forms of AI that promote sustainable ways to achieve food and nutrition security. Currently, there are three main applications of AI usage in agriculture.
Present Applications of AI in Agriculture
- Agricultural Robots – Some are using robots to perform essential and time-consuming agricultural tasks at a faster pace. For example, robots can harvest produce at a faster rate than human laborers with significantly reduced physical toil. One company that creates such robots is Harvest CROO Robotics. The company’s most recent development is a robot that picks and packs strawberries; it can harvest eight acres of berries a day and replace 30 human laborers per machine. By utilizing these robots, companies can improve productivity and increase yield.
- Crop and Soil Monitoring – Using image recognition, AI can use cameras to analyze soil quality and identify possible defects and nutrient deficiencies. Tech startup PEAT has made strides in soil monitoring AI in its development of Plantix, a deep-learning application that correlates foliage patterns with soil defects, diseases or plant pests. This application allows farmers to identify issues with soil quality quickly, allowing them to address any issues before the crop experiences damage.
- Predictive Analytics – These AI systems analyze data to make predictions about future outcomes. In agriculture, predictive analytics can improve market recommendations, pest modeling and crop yield predictions. This valuable information provides farmers with more certainty in their product outcomes while also cutting back on resources that they lose due to unforeseen circumstances. Precision Farming is one company that uses data from satellites and drones, such as temperature, precipitation and solar radiation, to predict weather conditions and plant nutrition.
Working Towards Sustainable Development
AI use in agriculture is allowing farmers to be more precise in their crop cultivation, producing a higher crop yield and quality. Agricultural robots optimize human activity and improve working conditions for farmers, while crop and soil monitoring and predictive analytics systems allow farmers to use resources more efficiently. This promotes sustainability in agriculture, as more successful produce outcomes cause farmers to waste fewer resources.
These AI systems contribute greatly to soil and water conservation. The Agricultural Stress Index System (ASIS), an indicator developed by FAO, is a computer that uses satellite technology to monitor areas that are highly susceptible to drought and water stress. Drought is the most damaging natural disaster to livelihoods, especially in developing countries. Therefore, predicting and addressing conditions of drought before they cause large-scale damage not only conserves water in times of need but protects human livelihoods. The implication of this is that more farmers, especially in developing countries, will have the means to support themselves and their families.
Fighting Food Insecurity
Prior to the spread of COVID-19, 135 million people were battling food insecurity. Now, the pandemic has exacerbated this problem affecting agricultural yields and livelihoods. The pandemic has impacted regions that normally depend on imports to support their populations the most, including Africa and island states.
Therefore, AI usage in agriculture in these regions can make a significant difference for populations that may already be struggling. FAO’s WaPOR portal monitors water usage through remotely sensed derived data over Africa, allowing for water and land productivity assessments. Saving valuable resources makes a crucial difference for countries that must rely more on domestic materials due to the present circumstances.
In addition, the United Nations’ World Food Program (WFP) is implementing a tracking unit that is collecting data to expand remote food security monitoring to 40 countries. The map quickly identifies food security emergencies and allows for quick response, helping humanitarians make evidence-based decisions on how and where to address food insecurity that could be damaging a population. By decreasing the time it takes for people to address these issues, the WFP is able to amend food insecurity for more regions in a shorter period of time and prevent them from deteriorating into situations of malnourishment.
With all the strides that have already occurred in AI and its applications, it is easy to forget that the technology is new and has vast untapped potential. As the industry continues to develop, farming will expand as AI usage in agriculture overcomes more issues challenging greater yield, sustainability and food security.
– Natasha Cornelissen