Role of IT for smarter agriculture

The intention is to deliver a solution oriented by Asset/Work management for Agriculture Industry, integrating operations and maintenance in order to improve agricultural productivity and make efficient use of natural resources (water, land, etc.). It comprises new IOC and Maximo functionalities, embedded ILOG Visualization Enterprise(JViews) for operational activities, ODM to manage incoming sensor's data, Cognos for reporting, Geo Spatial and integration points between those products (varying of data, process or user interface, depending of needed). * It still under analysis if ILog JViews Maps can replace ESRI for GIS or if they can be combined. Any recommendation is welcome. Value: Improve agricultural productivity, efficient usage of natural resources and reduction on the farming on the environment which can be a new market for IBM. Solution to Problem 2 Data collection for Desalination of water for irrigation The project collects a stream of real-time data on water quality, aquaculture, chemical content, all intended to inform and support not only major policy decisions, but also a host of industries in and around India

Applied Engine on Nature - AEON into our practice

With AEON into our practice: •Machine-driven powerfulness helps to achieve accuracy in data cleansing •First of its kind to produce the right sizing in the Machine learning dataset and improves accuracy in prediction •Brings the right balance between over fitting and under fitting in terms of quality in data sizing of Machine learning models •Human intervention is eliminated with appropriate solutioning - AEON brings high productivity with delivery efficiency improved by 40%. - The KPIs measure the effectiveness of API in creating a large dataset. Indicators are - 96% accuracy in AI prediction, -The data preparation is fully automated. - From the customer perspective, cost savings in the cloud billing is - 35 % in the design stage - and further 20 % in cyclic Bigquery execution in data analysis - and iterations of the training dataset for Machine Learning models. The algorithm brings a high-quality deliverable to the data pipeline. The regress functional, non-functional quality assessment has been proven in any big data engineering conditions. - AEON workflow is completely scheduled. - AEON sets right to tune in Machine Learning needs. The AEON aims to reduce the impact of the risk (weather, biological, price) faced by rainfed area farmers. The Nature Labs will facilitate the training of local resource persons/rural extension workers in utilising the tools for deployment with rainfed farmers. Nature Labs is also expected to develop a policy brief and recommendations at the end of the assignment. Concept/Product/Technology Overview - Highlight the uniqueness of the product or service or technology First, it should be a platform which takes sincerity and authenticity as priority and covers many fields, such as: IOT(internet of things), traditional industrial control technology, automation, could platform, big data processing, electronic commerce, social networking, ERP, business intelligence, electronic clearing and other technologies, it also include B2C、B2B、O2O and other business model electronic platforms. Second, this platform shall be operated like an enterprise. It is a platform that integrates all interests of every party. To operate this platform in business manner is so important that it can ensure healthy development of the platform, because no enterprise can invest all the time without profit. Different types of enterprises will join in the industrial chain, such as planting, breeding, processing, agricultural logistics, warehousing, agriculture insurance, retailers and consumers, to form a good and standard industry format. Government, as a regulator, can provide administration, supervision and other services to the platform. The government can also grasp the industry situation and give macro guidance by leveraging business intelligence technologies. The platform can be divided into four layers. The first layer is Basic IOT (Internet of Things) Application Platform. This is a platform aims to integrate enterprises ERP application, peasants or agriculture enterprises planting or breeding applications to the basic IOT Application Platform by leveraging standard IOT interfaces. It is designed to solve the following problems: first, planting and farming enterprises can upload and share land information, planting and breeding process information, planting and breeding planned picking data and actual picking data, safety monitoring data in a way of automatic or semi-automatic collection to the platform by leveraging their own assets or the management system we provided through IOT interfaces. Enterprises can not only manage operation by leveraging their own assents or ERP system we provided, but also upload and share any information to the platform through IOT Interfaces. For example: production plan and spare production capacity in manufacturing enterprises, transportation plan and available positions in logistics enterprises, storage condition and information in warehousing enterprises, etc. Community management can also be realized in this layer, which can contribute to promote peasants or merchants personalized brand products and services, at the same time, it can also provide a good chance for enterprises to communicate with relevant enterprises. The second layer is Public Service Platform. It can provide enterprises and customers (retailer, foreign merchant and consumer can be regarded as platform demander) financial service (including financial settlement, small loans, agricultural insurance, futures quotes and other services), expert service (custom planting and breeding technologies according to local soil and climatic conditions), expert technical instruction service (experts provide technical support to peasants and agriculture enterprises through telephone, network, SMS, video, etc.), agricultural assets service (pesticides, fertilizers, seeds manufacturers certification, sales, farm machinery lease service), agriculture technology and labor service, professional agro meteorological service, etc. This layer aims to insure peasants and agriculture enterprises can better develop agricultural production and take advantages of one stop procurement service. The relevant government departments can also make a transparency protection mode for peasants. The third layer is cloud transaction center. (Retailer, foreign merchant and consumer can be regarded as platform demander). Retailer, foreign merchant and consumer can purchase products and services. Accordingly to the sharing information provided by service provider in this platform, the system can automatically select service provider for platform supplier (peasant or peasant entrepreneur). Considering credit ranking generated in the platform, platform demander and supplier have mutual choice and find a satisfied way to fulfill orders. An order will be divided into several suborders and distributed to relevant company system for further processing. The result can be reflected in the platform system when the order is finished, and customers can give their comments and ratings for each service they received during the whole process. This layer aims to handle large volume orders by leveraging IBM automatic matching and optimization supply chain solution, which can seamless support large volume transaction. Retailer can make clear supply chain plan according to annual sales target and provide purchasing order information to the peasants or agriculture factory. In this case, retailer do not need to kicked around to organize their supply chain, at the same time, peasants or agriculture factories can arrange production accordingly to actual orders, which can highly reduce their sales risks. The fourth layer is monitoring and decision making platform. It can provide government regulators and platform operators visible data analysis, which can help peasants and enterprises manage their planting, breeding and production by making decisions based on aforementioned visible data. For example: If the platform system finds some vegetable cultivation is far greater than the market demand history, experts will give relative peasants risk warning through expert platform to make a flexible arrangement, which can greatly reduce agricultural products overstock we often read in the media. This layer also provides a public quality traceability platform, which can provide all the node data and reports to the customers from planting to end customers. All you need to do is swipe the two trace code to get product information. The whole platform is open to any third party developers. It provides business development opportunities to all parties who can contribute to the platform, making the platform healthy and energetic while bringing users much more convenient. - Indicate current level of preparedness Partial content of our solutions have been validated and applied up till now. For instance, the agricultural planting system, food processing system, logistics transportation system and the terminal e-commerce platform we developed for ACE Group have accomplished integration, communication of data chains and the sharing of information. The online inventory transaction on this platform has been achieved, and we are being engaged in further discussion on the business implementation pattern of agricultural order transactions. What is more, the food delivered by this platform can be traced swiftly for its safety including information of seeds and the producing and processing control information. - Is this patentable We believe both of the platform architecture and business model can be patent. Moreover, the tracing process and the quality control points we designed for the insurance of food quality according to food categories can also be our patent. Our Understanding of the Problem The recent drought has resulted in heavy losses for farmers and food production has declined. Despite the many challenges imposed in a natural disaster, the unpredictability of pricing, the farmers are feeding the food for every one of us. To solve problems at their level, we must understand the depreciation of a key resource that could technically solve all the problems. The global population is headed toward 9 billion by 2050. To ensure that agriculture can meet future demands, the world will have to: · Reduce the million metric ton loss of crops to pests, disease, and inferior storage methods · Make better use of the crops already grown while using other inputs like water sparingly · Increase yields on existing lands Water is one of our most essential resources, yet much of the water we use every day is “hidden” as an indirect, yet critical, component of something else food, health, energy, transportation and more. And of all the water on Earth, only 1 percent of it is useable by ecosystems and humans. As the world’s population increases from today’s 7 billion to an estimated 8 billion in 2025, the demand for water will rise to satisfy the increased demand for food, particularly as meat consumption in global diets increase. “One barrier to better management of water resources is simply lack of data — where the water is, where it's going, how much is being used and for what purposes, how much might be saved by doing things differently. In this way, the water problem is largely an information problem. The information we can assemble has a huge bearing on how we cope with a world at peak water.” Source: Wired Magazine, “Peak Water: Aquifers and Rivers Are Running Dry. How Three Regions Are Coping”, Matthew Power, April 21st, 2008 We believe that by addressing the following four problems, we will be in a better situation to address the bigger picture. Problem 1 Lack of technology in pricing agricultural commodities Problem 2 Data collection for desalination of water for irrigation Problem 3 Watershed development in pilot villages Problem 4 Lack of accurate Demand forecasting tools - Awareness of water management for farmers, commercialization of organic farming and add value of agriculture products. Solution to Problem 1 Lack of technology in pricing agricultural commodities in India Currently there is an incubation project of this size which has been initiated in India.

Millets - specific Focus on crop choices

Following three phases needs to be followed in sequence for best results. 1. Phase one - Control moisture content / Drying in sunlight. 2. Phase two - Separation of equal-sized grain by sieving (Separate by passing through a sieve or other straining device to separate out coarser elements). 3. Phase three – Husking (The removal of covering). 4. Impeller adjustment techniques Increase/decrease the impeller inter groove distance (A long narrow furrow cut), motor RPM. The need for multiple husking and broken grains was noticed. End user products like biscuits, and cereal processing offer good opportunities for small-scale businesses because raw materials are readily available, most equipment is reasonably affordable and if the products are chosen correctly, they have a good demand and can be profitable. ● Small-scale food items like cereal processors are confronted with strong competition in the domestic and regional markets. ● To be profitable it is essential to have high-quality products, an attractive package where appropriate, and a well-managed business. The purpose of this manual is to guide the small-scale cereal miller and baker in the Nature Labs project venue to optimize their processing methods and implement GMP (Good Manufacturing Practices) and quality assurance schemes and build their technical capacity for improved market access and competitiveness. Different types of food processing can be categorized into 1) Primary processing (post-harvest operations including drying, milling, etc.). 2) Secondary processing (e.g. baking, frying, etc.) in which raw materials from primary Processing are transformed into a wide range of added value products that are attractive and add variety to the diet. We are an agriculture advisory and agriculture business. Much more than marketing products and services around the world, this means establishing deep roots locally and leveraging our multinational presence for operational advantage. ● Provide a description of the organization’s approach, methodology, and timeline for how the organization will achieve the objectives of the Terms of Reference (TOR). ● Provide a detailed description of how the management for the requested services will be implemented in regard to the TOR. Nature Labs has been associated with IBM GBS and Google cloud BigQuery Machine learning with Vertex AI for prediction. The adapted solution and timeline are proposed to meet the following requirements. AEON is the next-generation innovation service for the data journey in the hyper scaler. The technology landscape of AEON builds with data ingestion, refinement, and visualization. The SerpAPI connects to the data sources and generates the dataset as per the business scenario. The smart algorithm brings precision to data engineering. Unification is the key differentiator in AEON that reduces the effort on the data pipeline. The smart architecture framework is illustrated here. It follows the natural path toward the best possible cloud-native features. The technology pillars redefine the concept of a big lake to retaliate against any data flood. The scientific algorithm defines very specific roles for each pillar in corresponding to the business rules. AEON germinates an elegant dataset from any search engines and open datasets. The algorithm performs data cleansing and refinement with optimal sizing before the data is ingested into the data analytics engine. The design emulates distributed dataset processing for any machine learning model. To summarize the technology landscape of AEON SerpAPI for effective data collection, The python application with an algorithm for data refinement. Big Query operates for the machine learning models. Vertex Artificial Intelligence brings the finest prediction. Google data studio for reporting and visualization layers. Human-driven process of handling a large dataset in the agriculture advisory demand a large dataset size of a zettabyte (~ trillion gigabytes) and is error-prone. The inherent complexity related to data cleansing is very time-consuming and is a root cause affecting a prediction matrix decision process. This is still an unresolved and ongoing issue in global practice in Ag. Methodology of the proposal: In order to solve the said problem within the delivery timeframe, an algorithm was needed. Nature Labs has designed an algorithm: ● with the ability to handle any data sizing and data types ranging from structured to unstructured ● with high processing speed and quality in data cleansing ● with replicability in any industry ● without compromising the data integrity. Nature Labs has developed an algorithm named Applied Engine On Nature (AEON) is designed with: A bundle of machine learning models of BigQuery Machine Learning and Vertex AI The python application wraps with SerpAPI for google search and automatically generates the optimal dataset. The ML model trains on the generated optimal dataset. The AI-based algorithm is intelligent to search once, and the fetched results are read multiple times hence reducing the cloud utilization billing cost. The key features: automatic generation of optimal BigQuery as per ANSI standard creation of an external table schema with error-free SQL syntax embedded with dataset path. The proposed solution to meet the requirements: 1) Through a consultative design developed in-house digital transformation algorithm named Applied Engine On Nature (AEON) for output-1 and for output-2 the farmers producer organization will be engaged in the proposed states of Maharashtra, Himachal Pradesh, Karnataka, and Assam. 2) The Solution will be carried by field engineers at identified villages. They will be stationed and work closely with farmer’s community, Government agencies for the field work. The data ingestion process will start from the field level and outcome of AEON will be educate through the field engineers for the beneficiaries with our experiences senior leadership team for field study and field-based process. Project Execution Stage An in-house algorithm that Natue Labs developed is named Applied Engine on Nature. AEON has 3 pillars. ● BigQuery - Machine Learning. ● Vertex AI; and ● The python application is wrapped in SerpAPI. The application layer automatically generates the schema Data Definition Language irrespective of data types. This feature eliminates all the complexities in big data architecture. Aeon search once and fetched results can be visualized in multiple ways. It optimizes the cloud billing cost considerably. The project is in a steady state. Now, over 100+ practitioners are implementing it in more than 10 use cases. The comparison on the technology landscape before and after the implementation of AEON. Before AEON, multiple Bigdata engineering tools such as Hadoop, Spark, and Cloud database that is MongoDB were used. The variety of ML models consumed a lot of time & effort. AEON implementation brings unify data pipeline solution through a big lake connecting different data lakes and datasphere. Also distributing the machine learning data into different distributed workloads. A Convolutional Neural network was used agriculture images and linear regression was used for the tabular dataset. With the Vertex AI, the accuracy of prediction is improved. We achieved the solution with modern data orchestration. The algorithm simplifies the data pipeline by reducing the complexity of collecting data from multiple sources. 1) Nature Lab will work with farmers and state environment agency for collecting the information on farmers including women farmers, in the identified geographies and also determine the gaps in information availability and its usage to contribute to the overall TCP project objectives. 2) The location will be identified based on Composite Index (CI), Natural Resource Index (NRI), Integrated Livelihood Index (ILI), Dry Days, Drought Occurrences, Area under rainfed agriculture & Gender Ratio, etc. The location selection will be done in a consultative mode with NRAA. 3) The Nature Labs will work closely with existing government agencies that are already providing physical and digital crop advisories in the identified areas. The assessment and other relevant information from the local authorities and appropriate national authorities will be used to determine the interventions across the cropping cycle – pre-sowing, sowing to pre-harvest, and harvesting to post harvest. 4) The interventions covers all the following aspects ● Hyperlocal Weather forecast and alerts for the local area ● Ground water availability ● Soil typology, and soil health ● Crop advisories ● Pest and disease alerts ● Market linkages and price alerts ● Community engagement Output 1: Crop forecasting framework and model incorporating climate (weather), soil characteristics and market information developed and piloted to aid rainfed farmers to make informed decisions Activity Constraints and models identified and reviewed Inception/Kick off Activity Develop suitable models and framework Uniqueness of the best practice - Handling a large dataset in enterprise applications is inevitable. - Hence AEON's design perfectly matches the Data Growth of Agriculture enterprise. - Our solution is aligned as Enterprise Google Cloud Adoption Framework. - AEON is unique, as it brings the best possible accuracy in terms of prediction. - Also with the power of AEON, it brings the right optimal datasets for data cleansing. - We also strike the right balance to train the data, for Machine Learning Models between over fitting and underfitting. - In terms of measurable quantities, we are close to natural prediction, hence the name AEON. - AEON almost eliminated human intervention. - Can predict close to the human way of thinking. - And the results are very evident, and is already published in 3 international papers. Establish the technology base for delivering services to farmers Product awareness with farmers Training and capacity building of farmers Activity Piloting of framework Agro-advisory & risk mitigation services to farmers First season assessment Second Season assessment Policy Brief development Final project report including data interpretation Output 2: Capacities of rural extension workers enhanced to support farmers in making informed crop choices using the framework Activity Training module for rural extension workers developed Extension training manual updated Activity Rural extension workers trained for extension services The Nature Labs will deliver the overall objective of the assignment on developing & implementing forecasting/predictive techniques and tools using BigQuery machine learning and Vertex AI for multiple data points and big data analytics to aid vulnerable farmers and planners to make informed decisions on crop choices, particularly in rainfed areas. The Project dataset carry both the predictive model and a large dataset with structured and un structured with complete scientific mechanism in collecting the whether, soil, market data related to crop and types of crops which the Natural Labs experience around 25 years. Hence with the expertise with google cloud architect and the field engineer the complete solution will be delivered with in time period October 2022 to December 2023. The application design and overall workflow is illustrated below. The above workflow is perfect solution, which brings the above required objective.

On-demand and price movements and cropping practices for each rainfed

Project goal of Nature Labs: The goal of Smarter Agriculture is to create an Agriculture Command Centre (ACC): Centralized Cloud-based solution to provide Smarter post harvesting, Price Prediction, Agriculture pricing demand, Smarter energy. Implementation of agriculture cultivation model of Vertex Artificial Intelligence. Facilitating the problem-solving process through the use of Machine Learning. We use IBM tools available from Nature Labs Activity Kits and preach the benefits of millet as a cash crop and a healthy source of food. Using Nature Labs project management techniques, develop and implement an On Demand Community marketing tool that addresses the needs of farmers, traders, consumers, and others, enhancing the perceived value and consumption of millet as food. Among other things, promote new and traditional recipes and the use of millet for feasts and daily meals. During the implementation of the on-field activity, the following works are carried out as a part of data collection and prediction for AEON. ● Collect and analyze soil ● Scout fields and implement methods to control crop diseases, weeds, and insect pests ● Examines fields and adapt crops to specific soils or climates ● Help growers discover the best methods of planting, harvesting, and protection against climate and pests ● Provide clients with agronomic solutions and suggestions for improved results ● Analyses plant health ● Develop reports and present results to farmers ● Keep up to date on all agronomy services, technology, solutions, and trends ● Assure customer satisfaction of products and solutions Implementation of the prediction named AEON for the direct trading between farmers and consumers through Agriculture Command Centre (ACC) Promotion of risk-free farming through Nature Labs Farmers Producer Organisation (FPO). Crop forecasting framework and model incorporating climate (weather), soil characteristics and market information developed and piloted to aid rainfed farmers to make informed decisions, which are gender sensitive and socially inclusive. Brand name Aeon for the value-added Food from Agriculture commodities produced by farmers What is the issue/pain point that your product/solution addresses Ensure the food quality safety. Food industry has its unique features of multiple sources and a long industry chain. Besides, the time-bound and irreversibility decide that it is the hardest to supervise among all the industries. Think about it, food safety will be definitely threatened if any of the problems occur from any part like water, soil, seeds, fertilizer, pesticide, and any processing or circulation process. However, most of the existing supervision systems are the bottom-check style with a low proportion of sampling checks, which disables them to guarantee effective management and supervision during the entire process. The point is, the solution we put forward is to alter the current food safety supervision model, establish entire process supervision according to product categories, and accomplish an information management system for the whole industry chain. Through implementing the system platform planned by our solution, the effective control of the entire food process (agricultural raw materials, processing, storage, logistics transportation, distribution, and retailing) will be easily realized, thus ensuring food quality is safe and reliable, finally realize the visual trace of any sections. Farmers, relevant enterprises, food supervisors, and customers will all be beneficial from it. Improve current situation. The imbalance between supply and demand and the problem of information asymmetry is gaining concern. Due to the cut down of the middlemen, farmers will gain more profit from producing, customers will save more money from consuming. The system platform mentioned in the project can provide a transaction platform for customers, relevant enterprises, farms, and farmers. It is a specially designed trading model that aims at the long agricultural cultivation cycle and numerous uncontrollable factors. It can do inventory transactions like traditional e-commerce (eBay or Amazon), and at the same time, it can do business according to expectations and demand. Provide a platform. The platform is designed to accomplish communication and service for farmers, relevant enterprises, farming experts, and food managers. Interactions of new planting technology, the use of pesticides and fertilizer, the remote diagnosis of plant disease and insect pests, food processing technology, new varieties, and agricultural services can all be accomplished via this platform. The project investigated and demonstrated that in the next decades it is certain that population growth will increase the global demand for food. Such demand, considering an agricultural scenario, should be achieved by improving the way that the plantations are managed by the rural cooperatives. More food, less environmental damage, less water loss, and saving energy are some of the several factors that will be considered to build up an effective agricultural business from a green perspective. Some of the causes of agriculture losses are unforeseen climatic impact, unplanned water for irrigation, and pricing for agriculture commodities. Currently, there are several patches to achieve such optimization and among them, there is the data analytical topic. Data can actually give us reliable information when it is well stored, managed, and, most importantly, analyzed accordingly. Once the correct information is accurate, not less than predictions, preventive maintenance, and quality control are possible to be executed in order to enrich and increase the efficiency of the agricultural market. IBM has proposed Agriculture Solution, Aeon Water, and Demandtec are proposed for Aeon initiative for Farmers club organization and pricing for agriculture pricing.

Agriculture Methodology of implementation

A drop of water or soil sample is placed on the AgroPad, which is a paper device about the size of a business card. The microfluidics chip inside the card performs on-the-spot chemical analysis of the sample, providing results in less than 10 seconds. The set of circles on the back of the card provides colorimetric test results; the color of each circle represents the amount of a particular chemical in the sample. Using a smartphone, the farmer would then take a single snapshot of the AgroPad by using a dedicated mobile application and immediately receive a chemical test result. AgroPad: “AI on the edge This “AI on the edge” computing approach uses machine learning and machine vision algorithms to translate the measured color composition and intensity into concentrations of chemicals in the sample, making it more reliable than tests based on human vision alone. Test data can be simultaneously streamed onto a cloud computing platform and labeled with a digital tag that uniquely identifies the time, location, and results of the chemical analysis. The cloud platform allows management and integration of millions of individual tests performed at various times and locations. This is an important feature for monitoring, for example, the change in fertilizer concentration in a particular region throughout the year. The proposed solution has a five-parameter prototype solution for soil and water testing that measures pH, nitrogen dioxide, aluminum, magnesium, and chlorine. We’re continually extending the library of chemical indicators available for deployment; each AgroPad could be personalized based on the needs of the individual user. The IBM research prototype, the AgroPad, enables real-time, on-location, chemical analysis of a soil or water sample, using AI. Since paper-based tests can be reliably performed by non-experts, public data collection with instant digitization in chemical sensing becomes a real possibility. Along with low cost, mass production of the paper-based device, and large-scale deployment through mobile and cloud technologies, the exploratory prototype could revolutionize digital agriculture and environmental testing. The soil, water, and environmental data acquisition will be carried out by means of the devices based on IoT, data will be streamed into Google cloud services Pub/sub and processed in the AEON Architecture.

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

Agriculture: Proposed Methodology, Approach, and Implementation Plan

Project Objectives The proposed assignment will contribute to the following overall impact and outcomes - Project Impact: Farmers’ resilience enhanced through abilities to anticipate and respond to climate variability and market dynamics in rainfed systems Project Outcome: Policy makers, planners, and individual farmers use forecasting techniques to make informed decisions on crop choices in changing climate scenarios and market dynamics. The project plan and requirement analysis 1) Design and development: (Output -1) a) Crop forecasting framework b) Weather - Model incorporating climate (weather) c) Soil characteristics and market information developed d) market information developed 2) Pilot the Framework and Model: The developed framework is the implementation in the locations identified 3) Policy brief and policy recommendation developed Output 1: Crop forecasting framework and model incorporating climate (weather), soil characteristics and market information developed and piloted to aid rainfed farmers to make informed decisions, which are gender sensitive and socially inclusive. 1) Crop forecasting framework and model developed and pilot implementation locations identified 2) Policy brief and policy recommendation developed Milestones for achieving (Output - 1) Requirements Milestones 1.1 Constraints identified plus a review of existing models developed Identifying the challenges related to 1) Climate parameters 2) Soil characteristics & soil moisture 3) Demand and price movements 4) Cropping practices for each rainfed area 5) Review existing models for crop forecasting through primary and secondary research. 1.2 Develop suitable models and framework 1) Develop a gender-sensitive and socially inclusive framework and models for the most important crops grown in the 4 identified areas a) To address the critical constraints support in crop planning activities b) Aid in proper management during the cropping season. 1.3 Piloting of framework Pilot the crop forecasting and planning framework across two crop cycles of Rabi 2022 and Kharif (rainy season) crops in at least one-two village in each of the four identified rainfed areas. Output 2: Capacities of rural extension workers enhanced to support farmers in making informed crop choices using framework 1. Training modules that are gender sensitive and socially inclusive for rural extension workers developed and trained rural extension workers, at least 10 workers covering one-two villages, in each of the identified locations in the state. Milestones for achieving (Output - 2) Requirements Milestones 2.1 Training module for rural extension workers developed 1) Training module for rural extension workers developed including selection criteria for rural extension worker a) handbooks b) Leaflets c) Posters d) video e) Other materials 2.2 Rural extension workers trained for extension services Rural youth identified and trained for providing extension services linked to crop planning model information. Solution approach & Implementation Procedure Implementation Approach: Nature Labs provide agriculture advisory and implementation services as follows. ● The objective of the consultative design and field-based process, will be delivered by Nature Labs ○ Determine the information needs of the farmers including women farmers, in the identified geographies ○ Determine the gaps in information availability and its usage to contribute to the overall TCP project objectives. ○ ● Consultative meeting and scope of location selection ○ Nature Labs will touch base with NRAA direction on the location identified ○ Collect the information on ■ Composite Index (CI) ■ Natural Resource Index (NRI) ■ Integrated Livelihood Index (ILI) ■ Dry Days ■ Drought Occurrences ■ Area under rainfed agriculture ■ Gender Ratio ■ any other parameters. ● Collaboration with the government agency ○ Nature Labs closely with existing government agencies ■ They are already providing advisory ● Physical ● Digital crop advisories in the identified areas. ○ The above assessment and other relevant information from the local authorities and appropriate national authorities will be used ■ To determine the interventions across the cropping cycle – ● pre-sowing ● sowing to pre-harvest ● harvesting to post-harvest. ● The interventions will cover the following aspects – 1) Hyperlocal Weather forecast and alerts for the local area 2) Groundwater availability 3) Soil typology, and soil health 4) Crop advisories 5) Pest and disease alerts 6) Market linkages and price alerts 7) Community engagement ● Utilize the existing project experiences ○ Nature Labs, where possible, will leverage the existing hardware of the state for the project implementation. ○ Outcome of the Nature Labs outcome will supplement the available information from the state and local authorities to make it relevant to the local conditions at the village level. ○ The log frame developed for generating advisories will be vetted by State Agriculture University (SAU)/ ICAR. ● Use of ICT ○ Nature Labs will use ICT (information and communication technology) tools, ■ mobile ■ web-based applications, ■ Appropriate technologies ● Sensors ● Automatic weather stations ● Any other hardware to implement the proposed interventions. Understanding the requirements Output - 1: Crop forecasting framework and model incorporating climate (weather), soil characteristics and market information developed and piloted to aid rainfed farmers to make informed decisions, which are gender sensitive and socially inclusive. Requirements Estimations* (*The tasks will be performed by 360 resources for the project duration of 15 months) 1) Crop forecasting framework and model developed 2) Pilot implementation locations identified ● Output-1:Develop-01 - Development of 675 man-days is estimated ● Output-1:Pilot-01 - Implementation efforts of 272 man-days are estimated 3) Constraints identified plus a review of eteristics & soil moisture, demand and price movements and cropping practices for each rainfed area, and a review of existing models for crop forecasting through primary and secondary research. ● Output-1:Identify-01 - Constraints identified efforts of 7 man-days is estimated 4) Develop suitable models and framework ● Output-1:Develop-02 - Development of suitable model efforts of 36 is estimated 5) Develop a gender-sensitive and social inclusive framework and models for most important crops grown in the 4 identified areas to address the critical constraints and support in crop planning activities and also aid in proper management during the cropping season. ● Output-1:Develop-03 - Development of important crops grown in 60 man-days are estimated 6) Piloting of framework Pilot the crop forecasting and planning framework across two crop cycles of Rabi 2022 and Kharif (rainy season) crops in at least one-two village in each of the four identified rainfed areas. existing models developed ● Output-1:Pilot-02 - Piloting of framework Pilot the crop forecasting and planning framework 40 man-days are estimated (Rabi & Kharif) 7) Identify issues related to climate parameters and soil characteristics. ● Output-1:Identify-02 - Identify issues related to climate parameters, soil characteristics efforts of 80 is estimated Output 2: Capacities of rural extension workers enhanced to support farmers in making informed crop choices using the framework 1. Training modules which are gender sensitive and social inclusion for rural extension workers developed and trained rural extension workers, at least 10 workers covering one-two village, in each of the identified locations in the state. A policy brief and policy recommendation developed Requirements Estimations* (*The tasks will be performed by 360 resources for the project duration of 15 months) 8) Training modules which are gender sensitive and socially inclusive for rural extension workers developed and trained rural extension workers, at least 10 workers covering one-two village, in each of the identified locations in the state ● Output-2:Training-02 - Training for rural extension workers efforts is 25 man-days are estimated 9) Training module for rural extension workers developed ● Output-2:Training-02 - Training module for rural extension workers developed, the effort is 40 man-days are estimated. 10) Training module for rural extension workers developed including selection criteria for rural extension worker (handbooks, leaflets, posters, video, etc.) ● Output-2:Training-03 - Training module for rural extension workers (handbooks, leaflets, posters, videos), the effort is 70 man-days is estimated. Rural extension workers trained for extension services Requirements Estimations* (*The tasks will be performed by 360 resources for the project duration of 15 months) 11) Rural youth identified and trained for providing extension services linked to crop planning model information. ● Output-2:Training-04 - Rural youth identified and trained for providing extension services linked to crop planning model information. the effort is 90 man-days estimated. 12) Capacities of rural extension workers enhanced to support farmers in making informed crop choices using the framework ● Output-2:Training-05 - Capability building informed crop choices using the framework effort is 120 man-days is estimated.