An Overview of Product Performance Prediction Using Artificial Algorithms

Artificial intelligence (AI) plays an essential role in enhancing productivity, reducing costs, and improving service delivery across various sectors, especially in agriculture. This paper offers a comprehensive review of recent advancements in AI applications for predicting agricultural product performance. Emphasizing the potential of AI, specifically machine learning (ML) algorithms, in precision agriculture, the study highlights its impact on crop yield prediction for crops such as rice, wheat, sugarcane, and soybean. The paper explores the benefits that AI brings to farmers' decision-making by enabling accurate yield predictions, addressing the urgent need for optimized resource utilization, and meeting increasing food demands.

The study focuses on the application of various machine learning algorithms, including supervised and unsupervised learning methods, to predict agricultural product performance. Algorithms such as regression, decision trees, support vector machines, and advanced models like artificial neural networks (ANNs) were analyzed. Several tools, including TensorFlow, Keras, and Scikit-Learn, were employed for model development and testing. These tools facilitated data handling and modeling processes, enabling the extraction of significant patterns from large agricultural datasets through data mining. This approach offers insights into critical factors affecting crop yields and helps refine AI models for more accurate predictions.

The performance of multiple machine learning algorithms was assessed by evaluating their ability to predict crop yield across various crops. Artificial neural networks, random forest, and support vector machine models demonstrated the highest accuracy in predicting crop performance, making them particularly suited for applications in precision agriculture. Despite the promising results, challenges remain, such as ensuring high-quality data, improving model interpretability, and adapting algorithms to specific agricultural contexts. Addressing these challenges can enhance the models’ practical application in real-world scenarios, allowing farmers to make more informed decisions based on precise yield forecasts.

This review underscores the effectiveness of AI-based models, particularly ANNs, random forests, and support vector machines, in predicting agricultural yield with high accuracy. By addressing limitations in data quality, model interpretability, and environmental adaptation, AI models have the potential to revolutionize agriculture, enabling farmers to manage resources more effectively and make data-driven decisions to maximize crop yields. The ongoing improvement of AI tools and techniques is essential for addressing the challenges in precision agriculture and meeting the global food demands of the future.