A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting

TL;DR

This paper introduces a voice-based decision support system combining machine learning models to help low-literacy farmers in India choose profitable crops based on agronomic and economic forecasts, enhancing their resilience.

Contribution

It presents a novel hybrid framework integrating suitability and market price prediction models with an accessible voice interface for marginalized farmers.

Findings

Random Forest accuracy: 98.5% in suitability prediction

LSTM forecast margin of error is low

System improves economic decision-making for farmers

Abstract

Farmers in developing regions like Karnataka, India, face a dual challenge: navigating extreme market and climate volatility while being excluded from the digital revolution due to literacy barriers. This paper presents a novel decision support system that addresses both challenges through a unique synthesis of machine learning and human-computer interaction. We propose a hybrid recommendation engine that integrates two predictive models: a Random Forest classifier to assess agronomic suitability based on soil, climate, and real-time weather data, and a Long Short-Term Memory (LSTM) network to forecast market prices for agronomically viable crops. This integrated approach shifts the paradigm from "what can grow?" to "what is most profitable to grow?", providing a significant advantage in mitigating economic risk. The system is delivered through an end-to-end, voice-based interface in the local Kannada language, leveraging fine-tuned speech recognition and high-fidelity speech synthesis models to ensure accessibility for low-literacy users. Our results show that the Random Forest model achieves 98.5% accuracy in suitability prediction, while the LSTM model forecasts harvest-time prices with a low margin of error. By providing data-driven, economically optimized recommendations through an inclusive interface, this work offers a scalable and impactful solution to enhance the financial resilience of marginalized farming communities.