Automation of Sowing and Harvest Planning in the Agro-Industrial Complex Using Intelligent Systems

This study addresses the urgent need to modernize agricultural planning processes amidst global challenges like climate change, population growth, and resource scarcity. We analyze the significant limitations of traditional, manual approaches to planning agrotechnical operations, which are often characterized by low accuracy, high labor intensity, and subjective decision-making. These drawbacks lead to suboptimal resource utilization, increased crop loss risks, and reduced profitability for agricultural enterprises. To overcome these issues, this paper proposes a comprehensive digital system architecture designed to automate, enhance accuracy, and improve the resilience of agricultural production. Our methodology includes a detailed "AS-IS" analysis of current business processes, identifying critical bottlenecks such as fragmented information and the absence of analytical tools. Subsequently, we present a "TO-BE" model, outlining a target intelligent information system. This system integrates key functional modules for automated data collection via IoT sensors, real-time weather integration through APIs, and advanced data analysis using machine learning algorithms for tasks like yield prediction, optimal sowing time estimation, and soil condition diagnostics. The architecture also features a user-friendly interface accessible via web and mobile applications, offering real-time monitoring, AI-generated recommendations, and automated reporting with integrated financial analytics. A comparative analysis vividly demonstrates that this digital transformation shifts agricultural management from a reactive, labor-intensive, and error-prone model to a proactive, data-driven, and predictable one. Implementing such intelligent technologies promises to significantly improve the efficiency, precision, and sustainability of agricultural management, ultimately bolstering food security and regional economic development.