Crop Selection by Machine Learning
Indian agriculture is being plagued by various problems. These problems directly and indirectly affect the life of a farmer. Farming practices and other activities of agriculture consume time as well as the efforts of a farmer. We stock grains and use food throughout the year. Since the 1960s, industrialised agriculture came into the picture. This has been successful but it is leading to a decrease in the variety of crops and livestock produced. Farmers have to face the issue of lesser rainfall due to improper irrigation. Mostly, the farmers suffer from infrastructural and economic problems in their routine life. This is mainly due to the lack of education and technical resources. If we stay on our current trajectory for food production, we won’t have enough food to feed everyone by 2050.According to recent statistics, over the next 35 years, production will rise 38–67 percent, but it needs to rise 60–110 percent.
The biggest problem with Indian farmers is the crop selection. They generally grow that product which was marketed at a great price last year. The use of cognitive technologies in agriculture could help determine the best crop choice or the best hybrid seed choices for a crop mix adapted to various objectives, conditions and better suited for farm’s needs.AI has the potential to positively impact soil health. Each soil tablespoon contains millions of microbes that form an ecosystem for the plant. By extracting the DNA from soil, analyze its microbial community, AI-based recommendations can be provided for maximizing soil health and crop yield. Machine learning algorithms can be developed to help determine which hybrids have the probability of achieving maximum yield potential in every environment.
Developing commercial hybrid products is a long and expensive process; it can take 7–8 years to determine how well the seeds grew, their resistance to pests and disease, and associated crop yields. By incorporating active machine learning, we can create a model that would offer a potential reduction in the footprint required for product characterization & commercialization and also provide valuable insights on predicted product deployment targets.
We can gather a lot of data, then use the data to try to learn patterns to be able to make personalized recommendations for each farmer. Active machine learning identifies the data most useful toward the end goal. Instead of using existing data, active machine learning algorithms can be designed which learns along the way. By understanding how seeds react to different soil types, weather forecasts and local conditions, machine learning algorithms would be able to suggest the best crop to grow. By analyzing and correlating information about weather, type of seeds, types of soil or infestations in a certain area, probability of diseases, data about what worked best, year to year outcomes, marketplace trends, prices or consumer needs, farmers can make decisions to maximize return on crops.
Chatbots for farmers
Chatbots are conversational virtual assistants who automate interactions with end users. Artificial intelligence powered chatbots, using machine learning techniques, understand natural language and interact with users in a personalized way. Agriculture could also leverage this emerging technology by assisting farmers with answers to their questions, giving advice and recommendations on specific farm problems.
Even though farmers do their best to generate their income and earn their livelihoods, we need to reflect on agricultural problems and solutions. We should make sure that the problems of the agriculture sector are eradicated so that the lives of farmers are not hampered and food production meets the demands of rising population.
