Seminars

View all Seminars  |  Download ICal for this event

Novel Algorithms for Improving Agricultural Planning and Operations using Artificial Intelligence and Game Theory

Series: Ph.D. Thesis Colloquium

Speaker: Mayank Ratan Bhardwaj Ph.D (Engg.) student Dept. of CSA

Date/Time: Jun 23 16:00:00

Location: CSA Seminar Hall (Room No. 254, First Floor)

Faculty Advisor: Prof. Y. Narahari

Abstract:
Farmer distress in developing and underdeveloped countries is a very common phenomenon. In-spite of several efforts put in by the various governments, NGOs, private companies, and the farmers themselves, alleviation of this problem remains elusive. Some of the major reasons behind the low returns, and losses, faced by the farmers are the inherent uncertainty in agriculture, unaffordability of advanced technologies, lack of access to markets, etc. This dissertation formulates and solves some of these contemporary problems in agriculture using artificial intelligence and game theory techniques. . Novel solutions are proposed that assist the state administration and the farmers during various stages of the agricultural crop cycle, starting from the pre-sowing and sowing decisions and going right up to the marketing of the produce. These solutions are: PREPARE (Price Prediction for Agriculture), ACRE (Agricultural Crop Recommendation Engine), CROP-S (Crop Planning System), and PROMISE (Procurement Mechanisms for Agricultural Inputs and Services).

PREPARE (Price Prediction for Agriculture)
Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. PREPARE accurately predicts crop prices using historical price information, climatic conditions, soil type, location, and other key determinants. In this direction, an innovative deep learning based approach is proposed, which achieves increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. PREPARE works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. Accurate price prediction using PREPARE will significantly enhance the farmers and state administrations decision-making abilities.

ACRE (Agricultural Crop Recommendation Engine)
A key challenge faced by small and marginal farmers is to determine which crops to grow to maximize their utility. With a wrong choice of crops, farmers could end up with sub-optimal yields and low, and possibly even loss of revenue. ACRE (Agricultural Crop Recommendation Engine) is a tool that provides a scientific method to choose a crop or a portfolio of crops, to maximize the utility to the farmer. ACRE uses available data such as soil characteristics, weather conditions, and historical yield data, and uses machine learning/deep learning models to compute an estimated utility to the farmer. The main idea of ACRE is to generate several recommendations of portfolios of crops, with a ranking of portfolios based on the Sharpe ratio, a popular risk metric in financial investments. ACRE provides a rigorous, data-driven back-end for designing farmer-friendly mobile apps for assisting farmers in choosing crops.

CROP-S (Crop Planning System)
A mismatch between the crops produced by farmers and the respective market demands could potentially lead to large-scale crop dumping. This leads to huge financial losses and distress to the farmers. To alleviate this problem, CROP-S addresses the macro-level problem of district level or county level agricultural crop planning in any given state. Using CROP-S, the Government or any state administration can make an informed recommendation on which crop acreages (number of acres cultivated under each crop) to allocate in which districts or geospatial regions in a given state, so as to match the demand for the crops and maximize the profits for the farmers. CROP-S uses a the mathematical programming approach for maximizing the profits of farmers taking into account key determinants of farmer profits. CROP-S uses data about predicted demands, transportation costs, compliance ratios (fraction of farmers who will follow the recommended crop plan), and historical data about yields and prices to arrive at an optimal allocation of crop acreages to districts.

PROMISE (Procurement Mechanisms for Agricultural Inputs and Services)
Procuring agricultural inputs such as seeds, fertilizers, and pesticides, at desired quality levels and at affordable cost, forms a critical component of agricultural input operations. Farmer collectives (FCs), which are cooperative societies of farmers, offer an excellent prospect for enabling cost-effective procurement of inputs with assured quality to the farmers. The objective of PROMISE is to design sound, explainable mechanisms by which an FC will be able to procure agricultural inputs in bulk and distribute the inputs procured to the individual farmers who are members of the FC. In the methodology proposed, an FC engages qualified suppliers in a competitive, volume discount procurement auction in which the suppliers specify price discounts based on volumes supplied. The desiderata of properties for such an auction include: minimization of the total cost of procurement; incentive compatibility; individual rationality; fairness; and given business constraints. An auction satisfying all these properties is analytically infeasible. PROMISE uses a novel deep learning based approach to design such an auction.