Devising of an Efficient Multi-parametric System for Prediction of Crop Yield using Augmented Incremental Machine & Deep Learning Operations

Abstract

Agricultural research and management have become increasingly dependent on accurate crop yield forecasting. This paper introduces a comprehensive model that incorporates multiple parameters to predict crop yield with high levels of precision and accuracy. The applications of this research are extensive and consequential. Accurate crop yield forecasts can assist farmers, agronomists, and policymakers with resource allocation, crop selection, and land management decisions. Our model provides valuable insights for optimizing agricultural practices and increasing overall productivity by taking into account key factors such as weather, soil type and fertility, crop variety, farming practices, genetics, satellite imagery & samples. The proposed model contains several internal components, each of which serves an efficient set of distinct functions. Deep Q Learning is used to analyze the impact of weather on crop variety, allowing the model to account for the impact of precipitation, temperature, humidity, Sunlight, and resistance to pests and diseases. Deep Dyna Q Classifier is used to evaluate the impact of soil type, fertility, and farming practices, thus accounting for variations in nutrient availability, irrigation, fertilization, and pest pressure. The crop's genetics are evaluated using Auto Encoders and a VARMA Model, which take into account the impact of inherent traits on productivity. Moreover, relevant spatial information sets are extracted from satellite images using a ResNet101 Model process. The rationale for integrating these internal components is based on their individual strengths and their capacity to capture complex interactions between various parameters. Our model achieves exceptional performance through the utilization of deep learning, reinforcement learning, and statistical modelling. For predicting the yield of Mango, Cotton, Wheat, Bajra, and Rice Paddy crops, the experimental results demonstrate a remarkable AUC of 99.2%, precision exceeding 99.5%, accuracy of 99.8%, recall of 99.4%, and an impressive AUC of 99.2%. In conclusion, our multi-parametric engine provides a robust and effective method for predicting crop yield. Its superior performance is a result of its ability to seamlessly integrate multiple data sources and employ advanced deep learning techniques. This research paves the way for informed agricultural decision-making, allowing stakeholders to optimize resource allocation, boost productivity, and ultimately contribute to food security and sustainability levels.