AI in Agri inputs industry
Using AI, Machine Learning, and Analytics to manage distribution systems is proven to increase the Return on Investment (ROI)for agri-input players
Artificial Intelligence (AI) is playing an increasingly important role in virtually every industry; the agriculture industry is no exception.
When presented with data, AI connects the dots between disparate data points more efficiently than experts. In many instances, its ability to find answers to complex problems exceeds, by several magnitudes, that of skilled professionals. AIs prowess at recognising patterns in complex data sets means it quickly spots patterns professionals miss. It is for this reason that AI is being used in industries like agriculture and agriculture inputs; industries which traditionally relied little on high-tech software.
AI is being used in the agri-inputs industry in the following ways.
Optimising Distribution Systems
Using AI, Machine Learning, and Analytics to manage distribution systems is proven to increase the Return on Investment (ROI)for agri-input players. Consider that because supply chains in the agriculture industry are vast, it is tedious, expensive, and time-consuming for suppliers to understand precisely the requirements across distinct supply chains.
Without support from AI, suppliers must travel from farmer to farmer to understand their needs. The logistical hurdles this presents are vast. Considerable time must be spent with each farmer before his needs can be fully understood. Consequently, players in the agri-inputs industry end up using substantial resources to know how much demand for their products exists.
The vastness of agricultural supply chains can be surmounted by using AI. An AI system collects data about how much fertiliser, feedstuff, pesticides and other products have been supplied to individual farms in a region. Once such data is fed into the system, the system can predict with a high rate of accuracy how much agri-inputs have been used, stocked, and will be needed in the future. The predictive capabilities of AI allow players in the agri-inputs industry to enjoy a birds-eye view of how their products are being used. Armed with a complete picture of how much product is being exhausted, suppliers of agri-products can optimise supply chains.
AI considers far more data than a human expert. It can predict how much additional input ofa product is needed along a supply chain. It does so by considering metrological data and much more. Furthermore, should an area suffer from drought, an AI system can accurately optimise the supply chain to the afflicted area such that the impact of water scarcity is minimised.
An AI system receives continuous data from every supply chain and can recalibrate shipments after considering dozens of data points. As a result, enterprises in the agri-inputs industry that use AI discover they need to make fewer trips to rural supply chains. Experts have difficulty considering a handful of metrics to reach a valid conclusion; an AI system can consider several dozen metrics to reach a rational conclusion.
New Product Development
Like every industry, the agricultural sector is continuously innovating. Innovation is essential to increase yields and grow food for a rapidly growing populace. To create products useful to agriculture, the agri-input industry needs the capabilities of AI. AI can help uncover the impact of a new pesticide or fertiliser on crops. Firms that manufacture such inputs benefit from AIs intelligent insights.
AIs uses in agriculture are even striking. It can analyse how much of a particular input is likely to be consumed in an area. Furthermore, it can increase or decrease output after considering how much demand for a product exists. Real-time data shared with AI gives it continuous oversight over vast geographies. And it can vary its calculations to account for changes in demand due to seasonal variations. Experts use static information to assess demand- AI; however, uses real-time data to do the same. Hence AI more accurately assesses and forecasts market conditions compared to even a seasoned professional who has experience in the agriculture industry.
Authored by Mr Sameer Wadhwa, Vice President – Digital Consulting and Client Success, Visionet
