VISUAL NARRATIVES OF WEATHER–CROP DYNAMICS: INTELLIGENT FORECASTING AND PRECISION CROP PROTECTION USING GFLOWNET MODELS - Jethva, Harikrishna B.; Vivekanandam; Vocaturo, Eugenio; Vasava, Mehulkumar J. (ShodhKosh: Journal of Visual and Performing Arts, 2026)
A history of challenges in intelligent agriculture has been inaccurate modeling of weather-crops interactions, which is characterized by climatic variability, nonlinearity of crop responses, delayed interventions, and uncertainty in future weather patterns. Current methods including statistical prediction using control based on rules, independent machine learning predictors and traditional reinforcement learning have constrained myopic decision-making, ineffective uncertainty management and inefficient optimization of long-term policies. To address such shortcomings, this paper will recommend a GFlowNet-Enhanced Weather-Crop Interaction Modeling framework to intelligent forecasting and precision crop protection. The approach presents a combination of probabilistic weather prediction and Generative Flow Networks (GFlowNets) to explicitly learn several viable weather-action-crop evolution procedures to allow sampling of high-reward decision trajectories, as opposed to making use of an optimal forecast or policy. Such a formulation of decision in the generation is paired with sequential planning of crop protection to bring about the uncertainty of the forecast to the adaptive and resource-conscious interventions. The proposed model attains a higher forecast accuracy of 97.54 as compared to the baseline methods with a considerable improvement in crop loss reduction, resource utilization and reliability in response to extreme events. It is assessed on five dimensions and they vary: predictive accuracy, reducing losses of crops, effectiveness in using resources, flexibility in extreme weather conditions, and stability of decisions in long-term. Agro-climatic and crop management dataset evaluation Benchmark testing shows that agro-climatic and crop management strategies have steadily better performance than machine learning and reinforcement learning based baselines, such as reduced intervention costs, resilience to uncertainty, and seasonal policy generalization. The discussion in this paper will cover mathematics formulation, algorithmic architecture using GFlowNet, the comparison of the algorithm with other methods, and implications of the algorithm to a scalable implementation. The findings prove the generative decision modeling in a GFlowNet-driven approach to be a promising research agenda in building climate-resilient predictions and next-generation precision crop protection technology.