Small Farmers form major workforce across the agricultural industry and they make major economic force in terms spending money & improving overall rural and national economy. Profit maximization of crop yields is possible through predict crop prices in advance and to ensure farmers receive maximum price on their crops. High crop prices translated quickly into needed income for farmers. This extra income meant that farmers could buy new equipment, more food, clothing, and so on. The net multiplier effect is increase in Gross Domestic Product (GDP) and it underscores the fact that the GDP as a measure of economic well-being . In-time crop price prediction would have a huge impact on overall economy of the world. To augment small scale farmers with information and data services that enable them better plan crops with higher market price yields and reduce the risk of economic uncertainty and market shocks, we need machine learning enabled agricultural economic information services that provide real-time gauge into commodity price market and that provide advanced prognostic view to course correct, if any, unintended consequences. The goal is to put more money into the hands of small-scale farmers and reduce their overall agricultural debt and thus make them more prosperous.
The term Kisan originated from Sanskrit language, and it means Small Farmer. The term Vikas is from Sanskrit, and it means progress.
The charter of Kisan Vikas Data Science Program (KV-DSP) is enablement of small farmers through data science program.
The purpose of Kisan Data Science Program is to bring the maturity and data science expertise of our company to
the needs of farmers worldwide. We believe in democratization artificial intelligence for the enablement of
small farmers and the KV-DSP is flagship offering from our company to the small farmers worldwide.
As an organization, our goal is simple! We wanted to bring the best of technologies and data science to small and marginal
farmers! Our Kisan Vikas Data Science Program (KV-DSP) centralizes on the improvement of small and marginal farmers.
At its core, the KV-DSP ensures the farmer gets the wonders and beauties of Data science to solve the needs of farmer and
ensures the custom data science models developed create sustainability and maximize revenue!
The positive effect of creating a sustainable environment for farmers is multi-fold: it improves food security,
enhances buying power of small farmers, enhances gross domestic product (GDP) of countries, creates a world that is full of
possibilities for humanity!
We have developed several agricultural and specialty crop models for enablement of small farms farmers, and
marginal farmers across the world.
Our models span from stable crops such as wheat to high revenue crops such as cashews.
As with any other science and engineering discipline, Data science is complicated and involved process. Through (KV-DSP), we would like to optimize productivity and provide efficient operationalization of small to mid-farms across the world. Hanumayamma may provide limited scope, no-cost technical resources to Small Farmer in order to enable engagements related to small farm deployment or use of Hanumayamma technologies including,
for example, agriculture analytics, specialty crops analytics, and dairy analytics, creation 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
.
Request a copy of book published by our leadership on democratization artificial intelligence, please click the book icon.
Small Farmers & Producers Advanced Analytics to help farmers to optimize Crop patterns and reduce farm input costs
.
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 PredictFertilizer Price Predict Model is based on the World Bank Pink Sheet Data. Please note that the Machine Learning model developed considers the variance of Fertilizer Price depend on the demand for Commodities. For instance, more demand of Commodities for example Rice or Wheat exert pressure on production side. This indeed increases the consumption of Fertilizers. Second, on the Supply side, in order to make Fertilizer one of the important component is Crude Oil or Natural Gas (depend on the manufacture technique). By inputting both demand and supply side, we can at least provide Small Farmer on the trend of Fertilizer prices. As per the World Bank pink sheet data, the following are common Fertilizers used:
Many of the agricultural datasets (for instance, the World Bank, Food And Agriculture Organization of the United Nations, the United States Department of Agriculture, and other agricultural research institutions) are derived from census and Survey Data Sources. The inclusion of Internet of Things (IoT) enabled data sources is a major game changer and helps to develop a closed loop back system with near teal-time or real-time information insights. Hanumayamma Dairy IoT Sensors provide such advantage to small farmers across the world. Dissecting the interplay of macro level economics, commodity prices, demand factors, environmental factors, transportation & storage factors, and weather patterns is complex. Enabling the small-scale farmer with this information on a constant basis would provide a better planning tool, information services, and enable small scale farmer to join information & Information Communication Technologies (ICT). Application of advanced data science techniques, Natural Language Processing, and Econometric models to deliver easily digestible form would bring big data revolution to small scale farmer. .
Hanumayamma Analytics platform analyzes the credit to Agriculture from over 120 countries on the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses. Predicting commodity prices is a challenge and includes data science & econometrics overlay with macroeconomic factors, governmental regulations, subsidies, import/export, production, and demand356 [8]. The application of econometric analysis, advanced statistics techniques, and machine learning and artificial intelligence techniques provide understanding of price transmission, correlation, and causality.
The World is at a critical juncture.In 2020, nearly one in three people did not have access to adequate food - between 720 and 811 million people faced hunger. Compared with 2019 , 46 million more people in Africa, almost 57 million more in Asia, and about 14 million more in Latin America and the Caribbean were affected by hunger. Based on forecasts of global population growth, current deficit to feed people around the world, and increased demand for greener fuel & biodiesel, food security will remain an important economic development issue over the next several decades. As food-versus-fuel tension becomes more intense , the day will come when more agricultural products will be used for energy than food. Adding to the conundrum, the COVID-19 pandemic has changed the face of the earth in terms of supply chain, resource availability, and human labor and has exposed our vulnerabilities in food security to an even greater extent. In essence, humanity is at a critical juncture and what this unprecedented movement in our lives has thrusted upon us -- the practitioners of the agriculture and technologists of the world -- is to innovate and become more productive to address the multi-pronged food security challenges.
Across the world, small farmers are facing unprecedented challenges – a fact that can be witnessed through increasing rural poverty rates and the rapid decline of small farmers in the national US and other places. The numbers speak for themselves: In Europe, agriculture is the major employment provider with about 10.5 million agricultural holdings in 2016. Despite this, farm numbers, ironically, have been in steep decline for many years. Most of the EU’s farms are small in nature; two thirds were less than 5 hectares in size in 2016. These small farms play an important role in reducing the risk of rural poverty, by providing additional income and food. Like Europe, agriculture and animal husbandry are the major sources of income for the people in Asia. Agriculture is important for all countries of Asia and the Pacific, with more than 2.2 billion people relying on agriculture for their livelihood. Small and marginal farmers hold 86% of the agricultural landholding in India. Agriculture is by far the single most important economic activity in Africa. It provides employment for about two-thirds of the continent’s working population and for each country contributes an average of 30 to 60 percent of gross domestic product and about 30 percent of the value of exports. While Africa holds more than 60% of the worlds arable land, the continent’s share in global agricultural production remains low. Agriculture is largest employer in the Latin America and Caribbean (LAC) region. In 2018, 14.1% of total labor force in the LAC region was employed in agriculture. Countries. The LAC region experiences high incidence of poverty and extreme poverty in rural areas (48.6% and 22.5%, respectively) since 2017 and widening gap between rural and urban poor with high undernourished people in the region (39.3 million).
Artificial Intelligence play an important role in improving productivity of small farmers across the world and improve standard of living. Through Data Science and Machine Learning Techniques, a small increase in productivity improves financial wellbeing of small farmers and reduce the widen gap & rural poverty. Importantly, Artificial Intelligence is the defender of the last resort in protecting small farmers from the onslaught on their survival - increasing "industrialization" of agriculture by large industrial houses and the application of automation at the expense of economic stability & sustainability of small farmers. Put it simply, the competitive market landscape is no longer plane and equal. In the "David and Goliath” fight, the small farmers need the capabilities of advanced machine learning and data science that can help them to prepare onslaught of “industrialization”.
