Nine percent of the population in the world facing food insecurity. This means that the affected regions of the world are living without reliable access to affordable and nutritious food. Because of this, there is a need for a more reliable approach to developing strategies that will support the agricultural sector. Most countries in the world face food shortages and in some regions where hunger is rampant, people die out of lack of food. This article will discuss why researchers propose ways to apply AI to agriculture and conservation.
During a workshop at the International Conference on Learning Representations ICLR 2020, panelists were discussing hoe AI and the learning machine could be applied in agricultural challenges. This came out following the shortages of food in some countries in the world.
Although the affected countries try as much as possible to solve food shortage problems, factors such as pests and diseases, climate change and labor shortages seem to increase the problem. Because of this, researchers suggest that AI can help deescalate this crisis.
During the workshop, IBM scientists spoke about their work in Africa with digital models of crops used to forecast specific crop yields.
Recommendations for digital farm models
Digital crop models are beneficial to not only farmers but also governments, suppliers, and distributors of farm tools and products. The technology is also essential to the banks. This is because the technology helps them to keep track of market trends. The technology also assists in making effective plans and establishing policies as well as minimizing investment risks.
During the workshop, Software quality assurance lead, Akram Muhammed, noted that the population of the world is likely to exceed 8 billion in the next five years. He also noted that farm-able land will decrease by 20% by the end of this century.
Muhammed also noted in his speech that to solve the challenges of food security, there is a need to make the supply chain simpler, safer, and less wasteful.
Muhammed and his team further introduced IBM’s PAIRS Geoscope. This is a service meant to host and manage petabytes of geospatial-temporal data such as maps, to store satellite and weather data about each farm.
Watson Decision Platform for Agriculture
This technology allows engineers to obtain forecasts of yield after feeding in moisture readings. The moisture readings are taken at multiple depths, soil fertility and nutrients, workflow information, and farm practice.
Using computer vision to estimate grape yield
The computer vision system measures grape yield from images of grapevines. Accurate grape yield estimates are crucial for planning harvests of agricultural produce.
Predicting forge conditions with satellite imagery
There was a concern to predict livestock conditions for livestock in Kenya. The presenters spoke concerning struggles of the North-Eastern pastoralists who largely depend on food from livestock. They also depend on livestock for income. This concern was raised because the pastoralists are often unable to predict droughts.
The ideal predictive model would prevent the loss of livestock as well as hunger by analyzing public data. The model can also be linked to a platform that quickly important resources to the pastoralists. This is beneficial to pastoralists because it helps in covering the costs of household and livestock needs.