Agriculture has been the backbone of human existence. Since time is immortal. It has also seen much advancement over the years. Agriculture sector of Indian economy is the mainstay of the rural Indian economy. Presently this sector accounts approx. 14% in the GDP. Agriculture in India is the core sector for food security, nutritional security, and sustainable development & for poverty alleviation.Maintaining soil fertility, efficient soil fertility, most importantly time of farmers to gain better results. Precision agriculture technologies, awareness about the condition for example can optimize fertilizer applications.With technological advancement we gain better results as technology enhances the conditions of agriculture sector by monitoring and checking the status of the crops. It also helps the farmers making them aware of the advantages of using modern technology.Since most of the Indian population is based on agriculture, it is thus very necessary to make proper use of technology in production rather than using outdated technologies which hamper the yield and growth. In most of the cases we see the farmers rely on their instinct which in many cases can hamper their productivity. With the introduction of our product this thing will not going to happen any-more.With the use of this product we try to ensure the economic well being of the farmers to attain maximum growth in production level. Our main target would be to remove the middle man between the farmers and the market for whom the hike in price, inflation in the market occurs. The focus will be on implementing efficient farm management.Technological advancement results in low cost of production and minimizes the wastage of resources that leads to increase in output level.Our product is unique in every aspect possible and it is the first of its kind. We are using AI along with Image Processing for the data collection and manipulation. The processed data can be sent to a database using wireless communication. The device will have a user interface so that the farmers, owners of the land can communicate with it. The system will tell what to do after analysing the input data which can be either fertility of soil or the quality of the crops or any infectious disease. For this, it will surely increase the productivity and even the system will analyse the soil conditions its features and tell the farmers which crop would be more profitable for him to grow based on data collected all over India. The key points of the following project would be:- Integrating IoT in Farms and deducing relations between Soil and Environmental Factors with Automated sprinkler system. • Tracking Harvesting with hyperspectral Camera using drones • Incorporating Farm data into analytics for Optimizing yields including yield estimation using computer Vision and Yield predictive analysis • Geo Spatial Analysis of the farm using Satellite Imaging and Data Plotting • Building Optimized Computer Vision Models to detect and classify plant diseases • Deploying Model Analytics and Visualization on Web Application and Mobile Client for Real time Data Classification. •Analysis of Project Impact and Product Viability and Market
Agriculture, a $2.4 trillion industry, is a foundation of economies worldwide. Factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield. As a result, artificial intelligence is steadily emerging as part of the industry’s technological evolution.
Together with IBM Watson and The Weather Company, teams from IBM Research-Brazil and IBM Research-India designed and built a suite of agribusiness tools and solutions to help the agriculture industry use the power of AI to make more informed decisions about their crops – the Watson Decision Platform for Agriculture.
Underpinning the platform, IBM PAIRS GEOSCOPE processes some of the satellite data and serves as storage component in the current system architecture. By aggregating and analyzing terabytes of multi-layer geospatial data using machine learning and advanced analytics, PAIRS allows us to store and run queries on the geo-referenced data.
Four of the APIs included in this new platform come from our global labs.
Yield History and Forecast for Corn This API uses big data and machine learning to predict yield for corn crops two to three months in advance with only a limited amount of data and computing power. Our system enables high-speed yield forecasts at a very high resolution (20 meters), generating personalized insights for farmers. The models can also be used to determine yields for past growing seasons — critical for validation of agriculture insurance claims and risk, optimizing supply-and-demand chain logistics and predicting commodity prices.
Disease & Pest Indicators for Corn This API service predicts the risks in corn production, leveraging hyper-local weather forecast details (temperature, relative humidity, precipitation, etc.) from The Weather Company and crop specific inputs (sowing date, growth stage, etc.) to model the outbreak probability of various pests and diseases. It also considers transport of the spore that triggers the disease. The advance notice for disease could help farmers reduce pesticide usage and take preventive or curative measures to avoid any unexpected yield loss.
High Definition Normalized Difference Vegetation Index (HD-NDVI) for Crop Health Monitoring HD-NDVI uses geospatial and satellite data to identify crop type and crop growth stage at a high resolution, 30 meters. The insights from this API could be used to assess crop health, determine fertilizer, pesticide and irrigation schedules, validate crop insurance clams, predict yield, and reduce risk in commodity trading. With this level of insight, farmers could take preventive actions (pesticide application, fertilizer or nutrient application, etc) to preserve and improve the health of their crops.
High Definition Soil Moisture (HD-SM) HD-SM is a high resolution, real-time measurement tool that monitors soil moisture at multiple depths (up to one meter) using a combination of AI algorithms and physical models along with several satellite and weather model data sets. Satellite data is combined with terrain data (such as land type, vegetation type, atmospheric parameters and solar radiation) from land surface models which is used to simulate changes in soil moisture