Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency

The novel research incorporates high-level machine learning algorithms for optimizing agricultural performance regarding sustainability and resource efficiencies. By using random forests and SVMs, this work successfully achieved 92% prediction accuracy for crop yields and an 89% classification accuracy of agricultural regions, thereby highly enhancing the decision-making power of farmers and policymakers. With over 10,000 historical records, the random forest model established a hypothesis that maize yields could be increased by almost 25% in ideal conditions. At the same time, the SVM identified more strongly within high-productivity areas a yield increase of 15% for targeted crops. Furthermore, Convolutional Neural Networks processed nearly 5,000 satellite images to register a precision rate of up to 94% for early crop stress resulting in a reduction in crop loss by 30%. Reinforcement Learning was used also to reduce water use in irrigation by 20% without impacting the yield of crops while optimizing irrigation schedules to adapt to real-time data concerning the environment toward helping to meet the sustainability goals. Convolutional Neural Network (CNN) stands out as the best algorithm in this context due to its exceptional performance in early detection of crop stress symptoms, achieving 94% accuracy. Findings have indicated that the multi-algorithm approach not only promotes increased predictive capabilities and resource optimization but also raises food safety with the increased threats in agriculture.