Small Farmers are a major part of the agricultural industry, and they have a significant economic impact in terms of spending money and improving the rural and national economy. Predicting crop prices in advance can help farmers maximize their profits from crop yields and ensure they get the best price for their crops. High crop prices can quickly generate more income for farmers. This extra income allows farmers to purchase new equipment, more food, clothing, and so on. The overall result is an increase in Gross Domestic Product (GDP), which reflects the economic well-being of the country. Timely crop price prediction can benefit the global economy greatly. The aim is to help small scale farmers with information and data services that can help them plan crops that have higher market value and lower the chance of financial instability and market disruptions. This requires machine learning enabled agricultural economic information services that can show the real-time state of the commodity price market and that can provide advanced foresight to adjust, if needed, any unwanted outcomes. The goal is to increase the income of small-scale farmers and decrease their overall agricultural debt and thus make them more successful.
The word Kisan comes from Sanskrit language, and it means Small Farmer. The word Vikas is also from Sanskrit, and it means progress. The aim of Kisan Vikas Data Science Program (KV-DSP) is to empower small farmers through data science program. The goal of Kisan Data Science Program is to apply the maturity and data science skills of our company to the challenges of farmers worldwide. We believe in making artificial intelligence accessible for the benefit of small farmers and the KV-DSP is our main offering from our company to the small farmers worldwide.
As an organization, our aim is clear! We want to provide the best technologies and data science to small and marginal farmers! Our Kisan Vikas Data Science Program (KV-DSP) focuses on the enhancement of small and marginal farmers. The KV-DSP ensures the farmer benefits from data science to address the farmer's needs and ensures the custom data science models developed create sustainability and increase revenue! Creating a sustainable environment for farmers has many positive effects: it improves food security, increases buying power of small farmers, increases gross domestic product (GDP) of countries, creates a world that has more opportunities for humanity!
Our models cover a range of crops that can help small and marginal farmers around the world. We have created models for both staple crops like wheat and high-income crops like cashews.
Data science is a complex and technical field that applies to any other science and engineering area. We aim to improve efficiency and enable effective management of small to mid-farms around the world through (KV-DSP). Hanumayamma may offer limited scope, no-cost technical resources to Small Farmer for enabling engagements related to small farm deployment or use of Hanumayamma technologies such as agriculture analytics, specialty crops analytics, and dairy analytics, development of new functionality for the small farmer, or machine learning and data science work (each, a “Project”).
Please contact our team to
start KV-DSP engagement: AI Labs or General ML Team
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Small Farmers & Producers Advanced Analytics to help farmers to optimize Crop patterns and reduce farm input costs
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Agricultural Equipment Manufacturers (Fertilizers, Tractors, and others) Farm Heuristics help Fertilizer and Farm Equipment companies to optimize supply chain and reduce costs.
Food Industries Agricultural Co-operatives, Retail Food Chains, and Food Industries .
Research Laboratories Real-time Farm & Sensor Data will enable research institutions, Farm manufacturers, and Fertilizer companies to better plan product based on the geo-location & climate variants.
Try Our Fertilizier Price PredictWe use the World Bank Pink Sheet Data to predict Fertilizer Price. Our Machine Learning model takes into account how the Fertilizer Price changes depending on the demand for Commodities. For example, when there is more demand for Commodities like Rice or Wheat, the production side needs more Fertilizers. Also, on the Supply side, one of the key ingredients to make Fertilizer is Crude Oil or Natural Gas (depending on the production method). By using both demand and supply factors, we can give Small Farmer some insight into the trend of Fertilizer prices. As per the World Bank pink sheet data, the following are common Fertilizers used:
A lot of the agricultural datasets (for example, the World Bank, Food and Agriculture Organization of the United Nations, the United States Department of Agriculture, and other agricultural research institutions) are based on census and survey data sources. The use of Internet of Things (IoT) enabled data sources is a big improvement and helps to create a feedback system with near real-time or real-time information insights. Hanumayamma Dairy IoT Sensors offer such benefit to small farmers around the world. Analyzing how large-scale economic trends, commodity prices, demand factors, environmental factors, transportation & storage factors, and weather patterns interact is complicated. Giving the small-scale farmer access to this information regularly would help them plan better, use information services, and connect with information & Information Communication Technologies (ICT). Using advanced data science techniques, Natural Language Processing, and Econometric models to provide simple and understandable form would bring big data revolution to small scale farmers.
Hanumayamma Analytics platform examines the agricultural credit from over 120 countries on how much money the private/commercial banking sector lends to farmers in agriculture, forestry, and fisheries, including household farmers, cooperatives, and agro-businesses. Forecasting commodity prices is hard and involves data science & econometrics overlay with macroeconomic factors, governmental regulations, subsidies, import/export, production, and demand. The use of econometric analysis, advanced statistics techniques, machine learning and artificial intelligence techniques help to understand price transmission, correlation, and causality.
The World faces a crucial decision point. In 2020, almost one third of people lacked sufficient food - between 720 and 811 million people suffered from hunger. Compared to 2019, 46 million more people in Africa, nearly 57 million more in Asia, and around 14 million more in Latin America and the Caribbean were affected by hunger. Based on projections of global population growth, current shortfall to feed people around the world, and increased demand for greener fuel & biodiesel, food security will continue to be an important economic development issue for the next several decades. As the pressure of food-versus-fuel grows more severe, the time will come when more agricultural products will be used for energy than food. To make matters worse, the COVID-19 pandemic has transformed the world in terms of supply chain, resource availability, and human labor and has revealed our weaknesses in food security to a greater degree. In short, humanity faces a critical situation and what this unparalleled shift in our lives has forced us -- the practitioners of the agriculture and technologists of the world -- is to innovate and become more productive to tackle the complex food security challenges.
Small farmers around the world are dealing with unparalleled difficulties - something that can be seen in the rising poverty levels in rural areas and the fast disappearance of small farmers in the US and elsewhere. The numbers speak for themselves: Agriculture is the main source of jobs in Europe, with around 10.5 million agricultural holdings in 2016. However, the number of farms has been dropping sharply for a long time. Most of the EU’s farms are small, with two thirds being under 5 hectares in 2016. These small farms help prevent rural poverty, by giving extra income and food. Farming and livestock are the main ways of making money for the people in Asia, as they are in Europe. Farming is vital for all countries of Asia and the Pacific, where more than 2.2 billion people depend on farming for their living. In India, small and marginal farmers own 86% of the farmland. Agriculture is the main economic activity in Africa. It employs about two-thirds of the continent’s workforce and accounts for an average of 30 to 60 percent of each country’s gross domestic product and about 30 percent of its export value. Despite having more than 60% of the worlds land that can be farmed, the continent’s contribution to global agricultural production is still low. The main source of employment in the Latin America and Caribbean (LAC) region is agriculture. In 2018, 14.1% of the total workforce in the LAC region worked in agriculture. The rural areas of the LAC region have high levels of poverty and extreme poverty (48.6% and 22.5%, respectively) since 2017 and a growing disparity between rural and urban poor with many people suffering from hunger in the region (39.3 million).
Artificial Intelligence contributes to enhancing the efficiency of small farmers around the world and raising their quality of life. By using Data Science and Machine Learning Techniques, a slight boost in efficiency can increase the financial welfare of small farmers and decrease the growing gap and rural poverty. Moreover, Artificial Intelligence is the last line of defense for small farmers against the threat to their existence - the rising "industrialization" of agriculture by large corporations and the use of automation that undermines the economic stability and sustainability of small farmers. In simple terms, the market competition is not fair and level anymore. In the "David and Goliath" struggle, the small farmers need the abilities of advanced machine learning and data science that can help them to face the challenge of "industrialization".
