Precision Agriculture       

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Precision Agriculture - overview


Overview

IBM Research India has started working on a special initiative on digital farming. One of the key objectives of digital farming/precision agriculture is to increase the farm productivity by increasing the visibility of agronomic states (such as soil moisture, crop health, weather, etc.) of farms, leveraging digitization, mobile, IoT and cognitive technologies. The team is working on developing a suite of solutions (pest risk prediction, plant disease/pest detection, crop identification, yield prediction, precision irrigation advisory services, etc) leveraging the power of IBM Research’s big data platform called PAIRS. The team is also involved in leveraging the power of mobile smartphone technology to capture field images and applying deep learning and advance image analytics to bring in actionable insights on time.

The team has released three agri sercices named "HD-NDVI", "HD-Soil Moisture", and "HD-Plant Pathologist" . The HD-NDVI service has the unique ability of blending different satellite data to derive a 30m-daily NDVI estimate. The objective of this service is to provide high temporal and spatial resolution NDVI for the region of interest over a period of interest. The service provides NDVI at a spatial resolution of 30m and temporal resolution of 1-2 days. This is achieved by blending satellite data from different satellite sources in a manner that allows estimating NDVI for required spatial and temporal resolutions. The service can be queried in four different ways depending on the region of interest and period of interest. The region of interest can either be a point in space or a polygon and period of interest can be the current day (nowcast) or an interval in the past (time series for a point or time lapse of images for polygon). The spatial and temporal extents of a query are the district and from January 1st, 2016 to current date respectively.  

 Soil moisture has a strong influence on several precision agricultural applications such as identifying crop health, determining optimal irrigation schedule etc. The existing soil moisture products from remote sensing satellites as well as model simulations are either not available or difficult to obtain at a farm scale, rendering these products of little use in precision agriculture. Keeping this in focus, we developed a HD-Soil Moisture service to provide soil moisture data at a high resolution at any location of interest in near real time. This is achieved by blending remote sense satelyte data with data from physical land surface model which takes input as land type, vegetation type, atmospheric parameters, solar radiation and simulate the evolution of soil moisture for required spatiotemporal resolutions.

The team has also developed a rich set of machine learning models to predict different pests and diseases for various crops such as paddy, potato, cotton, chilly, onion and tomato. We use different types of satellite data to monitor the crop health along with the highly accurate weather data to nowcast and forecast the chances of a particular disease to occur in an agricultural farm. Our models have the flexibility to work in different temporal and spacial resolution depending on the use case. We use novel parameter aggregation techniques to tune the models to work for different geographic regions.

The Plant Pathology app uses cognitive machine vision capabilities designed specifically to identify plant diseases and deficiencies manifested on leaf surfaces by diagnosis of associated visual symptoms. Our approaches also leverage technical pathology information to provide strong diagnosis based on actual plant pathology. 





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