An efficient yield prediction model using synthetic inference from L-systems

The ability to predict and estimate the harvest production is of vital significance to the food security and financial value of global agriculture. The use of synthetic inference in determining crop yield estimations cannot be underestimated. L-systems uses high-level estimation dependent upon inference techniques through sources ranging from real to synthetic data. This paper combines intelligent and highly visual features to infer crop characteristics. It provides a method of assembling superior synthetic datasets that reduce the reliance upon time-consuming fully trained neural networks. It demonstrates a mature approach to using synthetic inference that provides cost-effective reliable crop yield estimations. This model allows for scalable and affordable application of synthetic inference as part of an optimised yield-positive farming enterprise in crops such as wheat. By leveraging synthetic inference, digital twins, and visualization, scientists, agronomists, and farmers can gain deeper insights into improved crop management.