One of the most important tasks, globally, is to provide food of sufficient quantity and quality to sustainably feed and nourish the growing world population. In order to do that, according to the World Economic Forum, there is the immediate need to promote smarter agricultural growth which is triggered by Artificial intelligence.

Artificial intelligence benefits farming sector:-

Crop and Soil Monitoring

• AI also has the potential to positively impact soil health. Companies such as Trace Genomics are able to extract the DNA from soil, analyze its microbial community, and provide AI-based recommendations for maximizing soil health and crop yield.

Predictive Agricultural Analytics:-

• Various AI and machine learning tools are being used to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and more.
• Determining the right time to sow crops is often one of the biggest challenges for Indian farmers where drought and excess rainfall can be equally serious challenges. Microsoft India – AI-based Sowing App helps farmer with this.

Supply Chain Efficiencies:

• Companies are using real-time data analytics on data-streams coming from multiple sources to build an efficient and smart supply chain.

Disease, pest or nutrient deficiency :-

• Artificial intelligence (AI) app named Plantix identifies the crop type in the image and spits out a diagnosis of a disease, pest or nutrient deficiency.
• It also aids farmers by recommending targeted biological or chemical treatments for ailing plants, reducing the volume of agrochemicals in groundwater and waterways that can result from overuse or incorrect application of herbicides and pesticides.
• Allows farmers to slash insecticide and herbicide use, reduce damage to wildlife and human health, and minimize issues related to resistance to pesticides,.
• A group of researchers at Cornell University recently published research explaining how they built and trained a neural network able to identify brown leaf spot disease on cassava leaves with 98-percent accuracy.

Deep learning models:-

• Ag-tech companies are testing deep learning as a tool to boost productivity and reduce environmental impacts while helping farmers carry out tasks that people now perform.
• Deep learning models will help guide robots and drones in monitoring fields for pests and predators, tracking crop and livestock diseases etc.

Artificial intelligence applied to agriculture can potentially optimize and increase yields, improve farm planning, optimize resources, and considerably prevent waste.

Concerns :-

• AI advances could lead to greater efficiencies but also job losses.
• Deep learning models have improved a long way, but once you go outdoors, there are so many factors like lighting conditions, shadows, weather, dust and other factors that can affect their accuracy.
• If poor-quality images are fed into a deep learning model, the algorithm is more likely to misidentify the plant or its ailment.
• In India where digital divide is huge farmers will be unable to use this technology atleast in the short term.

Way forward:-

1. Farmers need to be equipped with training that is up-to-date to ensure the technologies are used and continue to improve. This will help to prove the value of these tools over the long haul.
2. Additionally, extensive testing and validation of emerging AI applications in this sector will be critical as agriculture is impacted by environmental factors that cannot be controlled unlike other industries where risk is easier to model and predict.

Conclusion:-

Society envisions a world where it moves beyond food pyramids, plates, and generic advice a world where DNA analysis, artificial intelligence, and food databases are combined for reaching tailor-made nutritional patterns.