Sensor-Based Cross-Modal Spatiotemporal Alignment and Causal Profit Modeling for Agricultural Input Optimization

Area of Science:

• Agricultural Science
• Data Science
• Machine Learning

Background:

• Smart agriculture is shifting towards optimizing input efficiency, resource conservation, and farm profitability.
• Integrating multi-source agricultural data (images, weather, soil, management) is crucial for precise water, fertilizer, and pesticide application.
• Challenges include inconsistent data frequencies, varied semantic scales, and difficulties in directly modeling input-profit relationships.

Purpose of the Study:

• To propose a novel framework, the Multimodal Agricultural Input-Output Optimization Network (MAION), for addressing the complexities of multi-source agricultural data integration.
• To enable precise agricultural input optimization and maximize farm profitability through data-driven decision support.
• To develop a method for estimating the effects of different input strategies on yield, cost, and net profit.

Main Methods:

• Constructing a unified plot-time-window agricultural state representation using a cross-modal spatiotemporal alignment module.
• Estimating the potential effects of input behaviors on yield, cost, and net profit via an input-output causal profit modeling module.
• Employing profit-driven reinforcement learning to generate input strategies for long-term net profit maximization.

Main Results:

• MAION demonstrated superior performance in yield, cost, and net profit prediction, achieving RMSE values of 0.587, 0.531, and 0.648, respectively, with R2 values of 0.914, 0.891, and 0.883.
• Significantly outperformed baseline models including Random Forest, XGBoost, LSTM, GRU, Transformer, Multimodal Transformer, and standard reinforcement learning.
• In economic decision-making experiments, MAION achieved a 17.68% Net Profit Improvement, a 1.71 Input-Output Ratio, and a 15.46% Cost Efficiency Gain.

Conclusions:

• The proposed MAION framework provides effective data-driven decision support for precision input, cost control, and profit optimization in smart agriculture.
• MAION's ability to integrate diverse data sources and optimize for long-term profit makes it a valuable tool for modern farming.
• The results validate the framework's potential to enhance farm profitability and resource management through advanced analytics.