AI-Plants-Diseases-SmartAgriculture

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AI-Plants-Diseases-SmartAgriculture

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Developing a real-life use case implementing AI for plants diseases in smart agriculture in localized context and leveraging open-source data, technologies, packages and tools

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# AI in Plants Dieases Use Case - Smart Agriculture
#### One of the main challenges confronting Agriculture is the planet disease that greatly affect farming field productivity and accordingly the whole supply chain of such prominent resources nationwide. The ability to detect, identify and proactively act against dieses could be an instrument of reducing or may be eliminating their negative economic impact on micro and macro levels. This could achieved in timely fashion by providing such identification services to farmers so they can use their mobile devices to spot and identify infection in their farming fields providing them with tactical action to help mitigate such risk as much as possible. On the other side, the identified diseases are collected and corelated to framing cycles, season schedules, climate, regions, economic factors to help decision makers updating policies and take strategic decisions for longer term planning.

#### This solution aims at employing cutting edge AI methods and techniques for images processing to enable disease identification, predictive anatectic and simulation with Spatial AI for what-if analysis and scenario planning to minimize the impact of infections and spreading. The setup will enable connectivity between centralized backend like cloud systems for model building /evolution and edge mobile devices for real-time disease identification. The simulation for what-if planning part will take place on the backend services to enable and should get updates in near-real-time or on demand upon request by a decision maker.

#### Who can benifit of such solution: Farmers – Extension Agents – Gov Planners – Gov Executives

#### 01 - Statement of Work: The SoW provides clear definition of the problem being solved and the associated challenges and pain points coming from the business domain context. It provided the scope and specifications of the solution being delivered.
#### 02 - Project Management: Planning, executing, and controlling the project in terms of scope, budged, time, quality, resources, and team assignment. Running project delivery in Lean Agile approach for lower risk and incremental delivered value.
#### 03 - Domain Understanding: Getting deeper into the context of the relevant business domains to understand strategies, policies, drivers, challenges, pain points and desired outcomes that represent value to the business, organization, community and/or individuals.
#### 04 - Solution Framing: Analyzing the challenges, pain points and at the same time the data in a business context to come out with solution elements especially the core machine learning models so an AI solution can deliver the desired outcome.
#### 05 - MLOps Design: As a first step for the integrated pipeline for delivering ML/AI solutions, the three layers of data, ML model and code pipelines are defined and designed to address the framed AI solution with needed CI/CD setup.
#### 06 - Sourcing Data: Identifying and implementing needed procedures and components to ingest required and supportive data/metadata from different sources using appropriate patterns for batch, real-time and streaming ingestion.
#### 07 - Modeling Platform: One key asset in an ML/AI project is the computing resources needed during developing the core AI models which typically requires high performance computing infrastructure as well as suitable technology components for manipulating data and applying processing and algorithms.
#### 08 - Data Preparation: Implementing and conducting different activities on the modeling to get the data loaded, quality-checked, transformed, reasoned, and explored and provisioned as preparation for the core modeling activities.
#### 09 - Model Planning: Start point of the core modeling by researching and exploring the potential models applicable to the given elements of the framed solution typically starts with features engineering and moves one to applying different ML/AI techniques to shortlist sounding ones.
#### 10 - Model Development: For potential models shortlisted on the model planning activity, models are taken to full model building in a setup emulating desired operational setup and workload to produce evaluation metrics and diagnostics against the actual operational workload.
#### 11 - Model Evaluation: The metrics and diagnostics as well as different applied parameters are compared so that one final model can be selected as the core for the AI solution taking into consideration the model efficiency as well as performance in the context of the required business outcomes.
#### 12 - Applications Development: Designing and implementing the application needed to make the core AI model operational including end user applications, integration with data sources, Microservices, data platforms, reports, dashboards, APIs among others.
#### 13 - Production Platform: Engineering, architecting, acquiring, and implementing the computing infrastructure needed for deploying and operating the full solution including data, ML/AI model and applications in an integrated way within the organization ICT landscape of other systems.
#### 14 - Operationalization: Planning a go-live with all solution components within DevOps setup including data, ML/AI model, application code and production platform ensuring all functional and non-functional requirements are to be met.
#### 15 - MLOps Rollout: Based on the MLOPs design as well as the operationalization plan, actual components of MLOps are deployed in production with the three elements of data, ML model and application code so that CI/CD can commence.
#### 16 - Solution Delivery: Defining and applying procedures to ensure the delivered AI solution components are solid and aligned with the customer organization’s ICT operations so that the solution continuously deliver value as intended.
#### 17 - Managed Services: Setting up teaming with the customer organization to outsource all or part of the operations, support, and sustainability of the AI solution to the project team on different levels of support.
#### 18 - Project Closing: Conducting final checks and validation of the different components of the delivered AI solution and handing over to the customer organization’s IT operations team.

AI-Plants-Diseases-SmartAgriculture

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