THE FARMS OF FUTURE: MACHINE LEARNING AND IT USES IN FARMING
Agriculture is one of the major revenue-producing sectors in India. Different seasons, market, and Biological Patterns influence crop production, but changes in these patterns result in an immense loss to farmers. These factors can be minimized by using a suitable approach related to the knowledge of soil types, pressure, suitable weather, crop type. whereas, weather and crop types and be predicated using a useful dataset that can aid to farmers by predicting the maximized profitable crops to grow.
1. Species Breeding
Species selection is a tedious process of searching for specific genes that determine the effectiveness of water and nutrients use, adaptation to climate change, disease resistance, as well as nutrients content or a better taste. Machine learning, take decades of field data to analyze crops performance in various climates and new characteristics developed in the process
For specialists involved in agriculture, the soil is a heterogeneous natural resource, with complex processes and vague mechanisms. Machine learning algorithms study evaporation processes, soil moisture, and temperature to understand the dynamics of ecosystems and the impingement in agriculture.
Yield prediction, one of the most significant topics in precision agriculture, is of high importance for yield mapping, yield estimation, matching of crop supply with demand, and crop management to increase productivity.
4. Crop Quality
The accurate detection and classification of crop quality characteristics can increase product prices and reduce waste. In comparison with human experts, machines can make use of seemingly meaningless data and interconnections to reveal new qualities playing a role in the overall quality of the crops and to detect them.
5. Disease Detection
Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area. ML is used as a part of the general precision agriculture management, where agrochemicals input is targeted in terms of time, place, and affected plants.
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