Bountiful Harvest: Enhancing Crop Production Prediction Using Ensemble Neural Network Model

Introduction: Research on agricultural yield prediction has been supported by applying several different machine learning techniques. Machine learning aids crop yield forecast. It can also help decide which crops to produce and what to do during growth. In a prospective research design, crop output has been forecasted by considering environmental, land, and water variables, as well as crop attributes. When attempting to forecast crop output, there are several elements that must be taken into consideration. These factors include the type of weather, the characteristics of the soil, the amount of water present, and the location of the farm. Objectives and Methodology: This study presents a pioneering methodology for predicting and classifying crop production, leveraging an Ensembled Neural Network Model (ENNM) that integrates the SqueezeNet and EfficientNet architectures. By amalgamating the strengths of these models, our approach aims to significantly enhance the accuracy and efficiency of crop production forecasting. SqueezeNet';s lightweight design and EfficientNet';s superior performance collectively contribute to a robust framework capable of tackling the complexities of agricultural data. Results and Discussions: The extensive experimentation on a diverse crop production dataset, we demonstrate the efficacy of our proposed ENNM, achieving an outstanding accuracy rate of 98.5%. This remarkable accuracy underscores the potential of ensemble learning and deep neural networks in transforming agricultural decision-making and empowering precision farming initiatives. The integration of SqueezeNet and EfficientNet within the ENNM framework not only facilitates more accurate predictions but also enhances the model';s adaptability to diverse agricultural environments and cropping systems. Our findings signify a significant step forward in harnessing advanced machine learning (ML) techniques for addressing real-world challenges in agriculture, with implications for improving crop yield forecasts, optimizing resource allocation, and bolstering food security efforts on a global scale.