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International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 11 | Nov 2024

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e-ISSN: 2395-0056 p-ISSN: 2395-0072

Agriculture Intelligence: Bridging Machine Learning and Human Interaction for Crop Optimization Pranav N. Sangave#1, Dr. Ganesh D. Bhutkar#2, Varad P. Uplanchiwar#3, Vedika V. Sontakke#4, Shrutika C. Gade#5 #Vishwakarma Institute of Technology, Pune -----------------------------------------------------------------------***-------------------------------------------------------------------Abstract—With the increasing adoption of precision agricultural productivity. The concept of leveraging data-

agriculture and the rapid advancements in machine learning technologies, there is a growing need to bridge the gap between artificial intelligence (AI) and human expertise in the agricultural domain. This research paper presents a novel platform that integrates machine learning models with human decision-making processes for crop optimization. The proposed solution aims to assist farmers, particularly new and inexperienced ones, in making informed decisions about crop selection by leveraging both AI-powered recommendations and the collective knowledge of local farming communities. The research methodology involves collecting and preprocessing historical agricultural data, training, and validating the machine learning models, developing a user-friendly Android application for farmer interaction, and conducting comparative analyses between AI-generated recommendations and human decisions. The platform's performance is evaluated based on metrics such as prediction accuracy, user satisfaction, and the potential for improving crop yields and profitability. The proposed platform contributes to the field of human-computer interaction (HCI) and machine learning in agriculture by demonstrating the synergistic potential of combining AI capabilities with human expertise. By providing a comprehensive decision-support system that leverages both data-driven insights and local knowledge, this research aims to enhance crop selection processes, optimize agricultural practices, and ultimately contribute to sustainable food production.

driven insights and local knowledge for crop selection is not as recent as one might think. Examples of such approaches date back centuries, when farmers relied solely on generational wisdom and manual techniques to make decisions about crop types based on their specific soil conditions and climatic patterns.

Keywords: Precision Agriculture, Crop Optimization, Machine Learning, Random Forest, Arima Time Series, Crop Price Forecasting, Farmer Knowledge Interaction, Sustainable Agriculture, Android, Local Knowledge Alignment

In the realm of agricultural intelligence, our system stands out by addressing the limitations of existing solutions. Unlike standalone approaches that focus on either machine learning models or human expertise, our research takes a holistic approach. Developing an integrated system that leverages Random Forest and ARIMA time series models for crop prediction and price forecasting, while seamlessly incorporating a farmer feedback module for knowledge sharing. This comprehensive solution combines advanced machine learning algorithms with a human-centered approach. By unifying these elements, the goal is to offer farmers a simple, all-in-one tool through a user-friendly interface for accurate and informed crop selection. The integrated approach enhances the overall effectiveness of the system in optimizing agricultural practices, providing a robust solution that harnesses the power of both data-driven insights and local knowledge.

I. INTRODUCTION In today's digital age, the seamless integration of technology into agricultural practices has brought both opportunities and challenges. One promising aspect is the rise of machine learning and artificial intelligence technologies in the domain of precision agriculture. These technological advancements are fascinating in the realm of crop optimization. As we explore the world of agricultural intelligence, it is disheartening to witness how the potential of these technologies has been underutilized, turning them from tools of innovation into missed opportunities for enhancing

Impact Factor value: 8.315

Farmers, both experienced and novice, have found themselves grappling with the challenge of selecting the most suitable crops for their unique circumstances. These instances highlight the growing need for robust decisionsupport systems that can harmonize the power of machine learning with the invaluable expertise of local farming communities, ensuring informed crop selection and mitigating potential risks to yields and profitability. In this era of rapid technological advancements, addressing the challenges of crop optimization through a synergistic approach becomes crucial. The following exploration delves into the increasing need for agricultural intelligence systems that bridge the gap between machine learning and human expertise, shedding light on the challenges faced by farmers across various contexts, but also outlines the objectives of the proposed solution in mitigating the limitations of existing approaches.

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