Agriculture crop choice prediction Execution plan and strategies

Nature Labs is a Non-Profit rural innovation research trust working on projects for agricultural-related challenges, along with other non-profit organizations. A total of 360 FTE will be deployed for 15 months. This comprises both the onfield engineers in Maharastra, Himachal Pradesh, Karnataka, and Assam and the delivery center in Coimbatore. The Agriculture Command Center (ACC) will be centered as per the required field locations proposed by the project stakeholder. The deployed engineers will work closely with the farmers, government officials, and other stakeholders throughout the project timeline. The resource onboarding process will be followed as per the CMMI standard of the project plan and will be shared with the stakeholder. The project requirements will be reused from the existing project artifacts from the ongoing activities in IBM On Demand initiatives, World Community Grid, Millets, and Aeon Water. Hence there will not be a delivery delay for the current requirement. We have planned to deploy 24 developers including AI, ML, Cloud architects, and SMEs in agriculture as per the project plan. Our initial plan is to deploy the core team for the design and deployment of the prediction algorithm. This approach will be of high quality as the average team experience is 15 years in the agriculture domain and they have been working on similar projects. In the month of October 2022, we will deploy 7 SMEs of the Global Delivery team in an agro advisory from our technology partner in Global Business Services International Business Machines Corporation (IBM). As the IT services leader, IBM solutions and services span all cycles of agriculture with diversity and breadth of project requirements. IBM is best at creating and delivering differentiating value to our farmers in India. The Nature Labs project team would comprise agriculture experts and software researchers from our technology partner. This project team will bring vast experience to the table. The development center for the prediction will be delivered from the on-field location as well as the IBM delivery center and Nature Labs’s project office at Coimbatore. Nature Labs will bring every element in agriculture tooling, process, solid, water, and seed processing along the portfolio spanning hardware, software, services, research, financing, and technology that separates IBM from other companies in the IT industry. IBM will bring their experiences in developing smart agriculture with the intention of Nature Labs teaming with FPOs and participating directly in consumer markets. IBM computational simulation on millets husking machine and Exchange of millets for education. Details of the completed project and reusable components Smart Agriculture – Aeon, prediction algorithm on agriculture inputs on soil, crop cycles, advisory crops and pricing for agriculture commodities, Production of more crops per drop of water through the optimized method of irrigation. The reusable component: Applied Engine on Nature - AI and ML Models Requirement 1.1 : Constraints identified plus review of existing models developed ● Identity issues related to climate parameters, soil characteristics & soil moisture, demand and price movements, and cropping practices for each rainfed area, and review existing models for crop forecasting through primary and secondary research. Solution for 1.1 : Constraints identified plus review of existing models developed Identify the issues related to climate parameters, soil characteristics & solid moisture, demand and price movements, and crop practices for each rainfed area, and review existing models for crop forecasting through primary and secondary research. Lack of accurate Demand forecasting tools - The management of farmers, commercialization of farming, and add-value of agriculture products. The IBM Intelligent smarter agriculture product family derives insights from data related to climate parameters, soil characteristics & soil moisture, demand and price movements, and cropping practices for each rainfed area, and reviews existing models for crop forecasting through primary and secondary research. The intelligent smarter agriculture product family collects the climate parameters ● Climate change attributes to affecting the soil fertility. As an event of altering various soil physicochemical characteristics. ● The mechanism to gather climate change and its effect on soil physicochemical characteristics. ● Soil characterization and mechanism of soil moisture with reference to the climatical condition. IBM Intelligent agriculture is designed to collect the soil characteristics, soil moisture, and vapor, the density of water layers on the surface to create agriculture value by delivering integrated insights. ● Implications of climate change in soil moisture, soil biodiversity, and microbial activities