GeoAI in Sustainable Agriculture Supply Chain ManagementUsing geospatial technologies with machine learning and blockchain optimizes agriculture supply chains

End-to-End Mapping of the Supply Chain 

1. Supply/ Produce Analytics

• Production Quantity
• Produce Quality/Grade
• Cost to Produce
• Time-to-Market
• Time to Process

2. Demand Analytics

• Market Penetration Mapping
• Market Share and Sales Forecasting
• Market Ranking
• Site Suitability Analysis
• Market Potential Analytics

3. Supply Chain Constraints

• Distance and Other Road Constraints
• Multi-Mode Transport
• Readiness for Storing, Processing, and Exporting Crops without
• Loss/Damage Compliance Parameters

4. Multi-Objective and Multi-Criteria Analysis

• Time to Deliver
• Volume/Quantity
• Revenue Price Profit
• Desired Quality
• Reduced Emission/Waste/Pilferage

Key Demand Analytics Features

1. Farming clusters or regions for seeds, fertilizers, and pesticides with quality and quantity. 

2. Demand clusters for produce supply.

A typical agriculture supply chain (below) shows the farmer-supplier through distribution or processing or collection hubs, often with many intermediaries. The produce is generated through multiple arrangements—contract farming, aggravated farming, and others.

EO analytics help provide the region-wise or farm cluster-wise production quantity and time-to-market based on crop phenology. This information helps to schedule the delivery of produce to 
demand points.

i. Crop Demand Analytics

Crop production volume depends on various parameters and is based on integrated analysis of these parameters such as selection of land (including soil, hydrology, and more), irrigation supply, and weather patterns, which helps farmers make informed decisions about when to plant, irrigate, and harvest their crops. This can help them maximize their yields and duce is then sold to minimize their weather-related losses due to droughts or floods.

Parameters that help with estimation of good and healthy production:

ii. Supply Analytics

The farmer-producer-broker-processing plant-delivery supply chain is a crucial aspect of the agricultural industry. It involves a series of interconnected steps that ensure the timely and efficient delivery of fresh produce from the farm to the end consumer. The process begins with the farmers who grow the crops and harvest them. The produce is then sold to brokers who act as intermediaries between the farmers and the processing plants. The brokers ensure that the produce is of high quality and meets the required standards. The processing plants then take over and transform the raw produce into finished products ready for consumption. Finally, the products are delivered to retailers and consumers through a network of distributors and logistics providers. This supply chain is essential for ensuring that fresh and healthy produce is available to consumers all year round.

iii. Solution Architecture

GeoAI is the application of MLOps in the geospatial industry. This involves the analysis and interpretation of geographic information from satellites, drone imagery, and data from other diverse, disparate sources—ground truth describing soil, land use, rock, crop and other data, and weather data. GeoAI automates the geospatial ML model life cycle and provides the necessary tools for model orchestration and continuous delivery and integration of geospatial ML models into production environments. GeoAI adoption shortens development and operational processes in the geospatial industry, leading to faster and more efficient analysis of geospatial data. Kubernetes and an automated model orchestrator are important components of GeoAI, allowing for scalability, efficiency, and high availability of geospatial ML pipelines.

iv. Geospatial Machine Learning Pipeline

A geospatial machine learning pipeline refers to the process of managing the end-to-end life cycle of a geospatial machine learning project. This involves data collection, pre-processing, model training, evaluation, deployment, and monitoring. 
Here's a high-level overview of the steps involved in a typical geospatial machine learning pipeline: 

1. Data collection: Gather geospatial data from various sources such as satellite imagery, GPS data, or GIS databases. 
   This data may include raster, vector, or point cloud formats and various data from secondary sources. 
2. Ingestion
3. Data pre-processing: Clean, pre-process, and transform the raw geospatial data into a format suitable for machine learning algorithms. This may involve data normalization, feature extraction, and data augmentation.
4. Data analytics
5. Output—dashboards, reports, published map, and APIs for downstream analytics.

Representative ML Pipeline Architecture over AWS

Benefits of GeoAI in Agriculture

• Enhanced decision-making: Empowers farmers with accurate and timely information, enabling them to make data-driven decisions to optimize operations, improve productivity, and mitigate risks effectively.
• Improved crop health and yield: Estimating early detection of crop diseases, pests, and other stress factors and addressing issues in advance optimizes crop quality and yield.
• Sustainable farming practices: Enabling precision agriculture techniques and optimizing resource usage.
• Effective environmental management: Identifying the right farming land with less erosion risk, efficient water resources, soil quality that demands less or zero usage of pesticides and fertilizers to reduce environmental impact.