Alleviating Agricultural Hazards with Artificial Intelligence

Did you know that 37% of our planet is solely used for farming! With increasing population and climate hazards, one of the most underestimated challenges humans are going to face in the coming decades is to improve and maintain productivity in agriculture. Artificial Intelligence aims to make agriculture as productive as possible so that farmers can reduce inputs, optimize yields and remove waste production.


Essentially most of the crop fields and farms extend for hectares, making it difficult for farmers to get an accurate idea of their farm’s condition. Many farmers use too many fertilizers in the field which proves to be a daunting mistake for them. This is where satellites and AI power are used and the concept of precision farming is introduced to farmers. Clearly, AI is transforming many industries and therefore, we are witnessing a huge demand for Artificial Intelligence Courses in Delhi and other parts of the country.


What is precision farming? 

Precision farming is a way of determining how the farms are optimized with a feedback loop based on data. Farmers can maximize farm efficiencies by introducing and adopting precision farming practices by tracking, evaluating and reacting to short-term experiments. Some of the benefits which farmers encounter by moving to precise agriculture are- improved farm’s economic advantage, minimized bad atmospheric conditions effect on the field and a perfect estimate of the number of fertilizers required.


Role of Satellites

Unmanned aircraft aka UAV was used highly in the field of agriculture but now satellites have taken the lead over the past few years. Satellites are used for large-scale imaging regularly. This is an advantage over UAV. However, satellites have some drawbacks too! Such as poor spatial resolution and data material or clouds which impact the imagery quality. If you join an Artificial Intelligence Institute in Delhi you will gain a better understanding of the impact satellites have in the field of agriculture.


Benefits of Satellites Imaging in Agriculture

  • Trouble identification and improved scouting activities with regular imaging and analysis. NDVI is the most common remote sensing tool for detecting problem areas.
  • Based on biomass evaluation during several crop seasons, many satellite imagery companies have prescription maps to automate the application of nitrogen. These VRA prescription maps can be downloaded to the Agri machinery to improve input production and field productivity.
  • Farmers can now easily track field performance using imaging and additional information gained through satellite-like temperature and rainfall.
  • Crops need the right amount of water to grow, anything more is a mistake that can damage the crops, this information is also provided by analysing the field through the data collected and the images captured by the satellite.
  • Every farmer’s ultimate goal is to reduce the input while maximizing the crops. With the use of satellites and UAV, trails can be conducted on a scale across the farm.
  • Farmers can easily compare large-scale farm fields and classify below-average fields to enforce the required treatment actions.


Do you know? ‘Crop Vigor which characterizes the health and strength of a plant is detected by NDVI.’


Is India using AI in Agriculture in the same way? 

Certainly yes! The Maha Agri Tech venture, launched in January this year, aims to use technology to tackle various risks ranging from poor rain to pest attacks. Accurate plant-wise and area-wise forecast provide information to make policy decisions like pricing, warehousing and crop insurance.


The Maha Agri Tech project used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad in its first phase to evaluate the average and select plant conditions. In its second phase, different data sets from various data providers will be combined to create yield modelling and a soil nutrient, precipitation, moisture and other important parameters geospatial database to facilitate location-specific advice to the farmers.


Satellite imagery has already helped determine the extent of plant devastation in parts of western Maharashtra during this August flood according to the officials. Using artificial intelligence- policy decisions and advisories ranging from crop suitability, stocks, crop damage monitoring, moisture condition, soil health analysis are possible.


MRSAC and NRSC are working for the coming Rabi season with expertise in satellite imagery analysis, as well as other agencies providing various types of data. Senior officials said the Maha Agri Tech pilot studies are key to long-term agricultural risk mitigation in Maharashtra, which has remained highly vulnerable to weather changes due to a very low percentage of farmland being covered by canal irrigation systems.


Commissioner of Agriculture, Suhas Diwase said, “the concept is to connect with different agencies to build a single digital platform for farmers and government. A GIS-based platform using satellite imagery-based algorithms and data analytics will also help monitor farmland in conditions of droughts. Maha Agri Tech’s phase one is over and in phase two we are expanding the scope and also validating ground truths to verify data.”


Past Module

Cropin, which has been in the Agri-Tech industry for nine years, claims it has collaborated with approximately 2.1 million farmers across 46 countries and 5.5 million hectares of farmland, evaluating 3,500 varieties and 365+ crops. This produced huge chunks of data sets, used by artificial intelligence systems.


Other indices produced by Cropin include the index of chlorophyll, the index of evapotranspiration, etc. It is possible to develop farm-specific intelligence on the acreage, health and yield in combination with information such as the right time of year for irrigation, climatic conditions and moisture pressure.


Future Module

It is estimated that future modules can analyze market conditions including analysis of commodity-wise demand and supply, location facilities and specific fertilizers and other inputs. Other government schemes may also be incorporated with the digital systems including soil health cards, Groundwater analysis, Watershed planning, etc. In the next level, water budgeting models can also be constructed using algorithms to analyze vegetation or moisture periodically.


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