CropLizer: An Agro-Socio-Edapho-Climatological Tool for Rice Nutrient Management and Profitability Assessment

Smallholder farmers frequently rely on thumb rules assuming that higher fertilizer inputs guarantee higher yields due to the absence of site-specific edapho-climatological data. This dependence on generalized rules creates a disconnect between site-specific requirements and field management practices, necessitating modeling field dynamics and providing actionable advisories to farmers. To address this disconnect, this study developed ‘CropLizer’ a machine learning and remote sensing based tool (https://mukundn1997-croplizer.hf.space/) to function as an integrated decision support system for rice cultivation. To develop CropLizer, this study synthesized a comprehensive dataset comprising over 45,000 rice field points (60% was reserved for training and the rest for validation) integrated with broadly yields, irrigation, nutrient practices, social status (education and ethnic group), climatic variables (precipitation), soil quality variables (carbon, nitrogen, and bulk density), as well as market accessibility. Subsequently, seven models (linear, support vector, decision tree, random forest, neural network, LSTM, transformer) were trained and hyperparameter tuned to predict yield and fertilizer requirements based on 43 agro-edapho-socio-climatological variables through ‘sklearn’, ‘tensorflow’, and ‘optuna’ libraries in python using IIT Roorkee’s super computer PARAMGANGA. After optimization, a web application was developed to allow users to simulate different scenarios by adjusting specific farming inputs to identify optimal management practices. Consequently, the system generates prescriptions for nitrogen and phosphorus and potassium application rates based on the predicted yields. Moreover, a user could find the potential yield for their field and what adjustments in field practices are required to obtain the potential yield sustainably (without loss of soil carbon). Considering the practical difficulties of gathering meteorological record and soil data, an application programme interface was set up for automatic retrieval of these variables from the field coordinates from open-meteo and soilgrids datasets. Upon validation, the performance of the best performing model (random forest) demonstrated a satisfactory accuracy (65%). Beyond agronomic parameters the tool calculates economic viability by integrating local market prices to estimate potential net profit margins and benefit-cost ratios under current yield and potential yield.  This framework bridges the gap between scientific research and field application by providing assured predictions for pre-season planning to mitigate financial risks.