Ms. Le Thi Quy Nien Agriculture Solutions Sales and Distribution IBM Vietnam Smarter Agriculture

Transcription

1 Ms. Le Thi Quy Nien Agriculture Solutions Sales and Distribution IBM Vietnam Smarter Agriculture

2 Cognitive IOT Solutions: Digital Precision Forestry Agricultural, Industrial & Infrastructure IOT Solutions Cognitive IOT Platform 01 Use Case / Domain Knowledge 02 Deep Analytics Patterns/ Pipelines 03 Data Curation/ Knowledge Engineering

3 Key Challenges in Agriculture and Forestry Low overall average yield, lot of variation within farm/region/plot Lack of ability to manage calamities Timely management of Pests, Diseases, Nutrient deficiencies, Droughts, etc. Pest Attack Disease Nutrient Deficiency Water Scarcity Whitefly Thrips Blasts IBM Corporation 1 February 2017

4 Key Challenges in Agriculture and Forestry Low overall average yield, lot of variation within farm/region/plot Lack of ability to manage calamities Better Advisory to Field officer/ Grower on precise control of inputs (variable rate within the farm) + Timing of Harvest, etc. by analyzing data from various sources will surely help Timely management of Pests, Diseases, Nutrient deficiencies, Droughts, etc. Pest Attack Disease Nutrient Deficiency Water Scarcity Whitefly Thrips Blasts IBM Corporation 1 February 2017

5 Example Use Cases Precision Harvesting Problem. Rain during harvesting or grain drying leads to yield loss. Solution. Schedule harvesting and drying based upon high precision weather advisories. Benefit. Reduce Yield Loss. Precision Irrigation: water conservation, yield maximization Precision Fertilization Problem. Fertilizer washed away by rains. Solution. Schedule fertilizer application based on high precision (1.5 X 1.5 Sq Km) weather forecasting. Benefit. Reduced wastage of fertilizers. Precision Pesticide Other Examples Precision Planting Disease Risk Models Can be exposed through easy to use interfaces such as mobile (Android or ios), and/or integrated into variable rate irrigation or variable-rate drone- based dissemination systems. Problem. Pesticide and fungicide applied on predetermined schedule even if there is no attack. Also, rain can wash away applications. Solution. Model pest and disease growth dependence on weather. Use integrated high precision weather forecasting and pest / disease model to determine pesticide / fungicide application schedule. Benefit. Reduce pesticide / fungicide loss. Reduced impact on environment.

6 PAIRS*: A big data platform for scalable spatio-temporal data and analytics Ø Data bus feeds in (near) real-time open spatio-temporal data into PAIRS (Physical Analytics Integrated Repository and Services) Ø Full data curation process (filtering, classifiying, aligning, resampling, reprojecting etc) at ingestion. Ø Large-scale Hadoop / Hbase system for efficient distributed data store and processing Ø System allows complex queries, e.g. Find all real estate in California with elevation gradient and high rain fall and certain soil type. Ø Access to new layers of analytics Irrigation forecasts Improved weather forecasts Ø Curated data and analytics accessible as a Service via an integration layer REST APIs to run queries Basic web interface to run queries * Physical Analytics Integrated Repository and Services IBM Corporation 1 February 2017

7 Internet of Things (IoT) - Sources of Data for Precision Agriculture Seeding/ Planting Sensors GPS/GIS Data from Satellites Social Media Weather Data Crop Modeling Drone Sensor and Image Transmissions Application Sensors for Inputs Internet of Things Pivot Irrigation Sensors Soil Moisture Sensors Environmental Data temp. humidity, wind, heat units Yield Sensors on Harvesting Equipment Nitrogen Sensors Drip Irrigation Sensors

8 Cognitive Blending of Remote/Local IOT Data to Maximize Insights Dynamic Agronomic Information Soil Moisture NDVI Ref Evapotranspiration Crop Evapotranspiration. Environmental Factors Temperature Rainfall Humidity WIoT based Estimate Plant Physiological State via Mobile Imaging, Local Sampling Local Information on Physiological state of the plant (or a set of plants) Physical sampling (eg: refractometer) Cognitive Blending & Analytics Engine Scalable Solution for Precision Agronomic Insights

9 Case Study: Disease / Pest Advisory System w/ Local Soft Sensing: Indian Client IBM agri camera Agri dashboard for visualizing agronomic states Pest prediction (early and late blight) for potato Vegetation Health index (NDVI) & Soil Moisture information IBM agri camera to capture images and feedback from fields (soft sensing)

10 Precision Agronomic Insights: Solution Framework End user App/ Dashboards PA Advisory Services Irrigation Management Crop Health Management Yield Prediction Data Curation (Remote/local/Weather) Soil Moisture Model NDVI Cloud Correction Crop Specific ET Model Agronomic Input Calibration using local data Data Sources (Remote/local/Weather) IBM Corporation 1 February 2017

11 Blended Remote/Local Soil Moisture (SM) Estimation Service Top layer: 5-10 cm Root zone: important region for agricultural applications, hydrological processes NASA SMAP MeTop ASCAT Local sensors like Tensiometers Soil moisture is the water that is held in the spaces between soil particles 1. Soil information (Harmonized World Soil Database) 2. Customer information 3. Crop information Applications of soil moisture sensing: 1. Agriculture (Irrigation scheduling, Yield Forecasting) 2. Drought Monitoring 3. Human health monitoring and prediction of heat stress and conditions for waterborne diseases Blending remote with local Depth calibration Volumetric interpolation Soil moisture estimate at any global location and time (maximal time/space resolution) Challenge: Local sensor deployment / maintenance is expensive Solution: Blend in remote sensed information to allow estimation at non-locally sensed locations. Status: Developed w/ Watson IOT BU. Available as a Bluemix MicroService (Value add to PAIRS)

12 10 acre yield map Precision Agriculture at Gallo Wineries : Applying irrigation forecasts to optimize yields and water efficiency Double drip line with control electronics. Yield maps (e.g. vineyard) o Large intra-field variability (measured during harvest) limits total crop yield o Changes by 2x within less of 20 meters Can the low performing parts of field be managed to improve o Crop yield, uniformity, quality and/or o Water efficiency? Satellite images are analyzed to determine greenness of wines (NDVI) surface temperature Predictive weather model Weather station data Evapo-transpiration modeling Control a smart variable rate irrigation system {can also deliver dissolvable fertilizers optionally} IBM Corporation 1 February 2017

13 NDVI map - Illustration Pockets of low vegetation health NDVI map can be used to monitor the vegetation health of the farm remotely It helps to find the sub-regions(marked in red) having low vegetation health in a large farm. It helps to detect/localize the nutrient deficiency or pest/disease stress.

14 Forecasts improved yield and water efficiency after 2 years of closed loop operation Yield Water efficiency IBM Corporation 1 February 2017

15 Use in India (eg: Precision Agri for Sugarcane) Sub-acre visibility into crop vigor characteristics IBM Corporation 1 February 2017

16 Thank you for your attentions!