Integrated IoT-based production, deep learning, and Business Intelligence approaches for organic food production

The organic food processing industry grapples with several complex challenges, such as ensuring the ingredients' authenticity, reducing resource consumption, and maintaining consistent product quality despite fluctuating demand and the supply seasonal nature. Previous methodologies often lacked integration of real-time data and advanced predictive analytics, leading to inefficiencies and increased waste. This study proposes a novel framework that combines IoT sensor networks, deep learning algorithms, and business intelligence to optimize production processes in organic tomato processing. By employing a Long Short-Term Memory (LSTM) model, the framework effectively predicts sales, manages raw material procurement and enhances logistics based on real-time data inputs. Findings indicate a 25 % improvement in productivity and a 20 % reduction in waste during production, alongside a 30 % increase in profitability attributed to informed pricing strategies and enhanced supplier quality management. The integration of predictive analytics not only aligns production with consumer demand but also supports sustainable practices by minimizing overproduction and waste. This work addresses the critical intersection of technology and sustainability in food production, ultimately contributing to a more resilient and efficient organic food supply chain. Keywords: Organic food, data mining, deep learning, Business Intelligence