Synthetic Agricultural Datasets for Computational Stress-Testing (CROP-LENS Framework)

These synthetic datasets were created to test the algorithmic scalability and high-volume robustness (up to 3,500+ rows) of the CROP-LENS framework. These datasets were created utilizing a Large Language Model initialized with actual agricultural data to ensure believable minimum and maximum limits for characteristics such as Temperature and Soil pH. Nonetheless, generative LLMs do not accurately represent genuine environmental multi-collinearity. For instance, these synthetic datasets include fabricated statistical associations (e.g., a created >0.80 correlation between humidity and rainfall, and inverted chemical correlations between Potassium and Phosphorous). Consequently, this information should not be utilized for any biological, ecological, or agronomic assessments. It is offered solely for the purposes of computational reproducibility and rigorous software testing.