“Bio-Agricultural Analysis&Forecasting for Crop Nutrients Management”

Abstract

The research thrust in preemptive analysis, classification, and validation has been advocating for the development of a framework for making predictive models ever since the need arose for powerful agricultural decision-making tools. Thus, in the current paper, an effort will be made to close the gap among existing avenues within the context of time-series forecasting by incorporating an ensemble, deep learning methodology for classification followed by a detailed analysis of each step entailed in the study, including rigorous validation through ANOVA. This is also one of the limitations, as they show low accuracy and inappropriate validation processes towards dynamic and complex environments, such as an agricultural environment with various scenarios, which require real-time decision support. Instead, the model proposed builds up increased predictive accuracy by initially understanding the critical agricultural parameters such as nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall through the use of SARIMAX. This is a preemptive strategy that the input data, in turn, reflects potential future conditions and thus improves the validity for a number of subsequent classifications. This is done for classification with an ensemble of deep learning methods that constitute K-scatter nearest neighbors, random forest, and linear SVC along with logistic regression and multinomial naive Bayes. That is, the classifiers were called upon because these are suitable for high-dimensional, non-linear data, thus giving robust performance for a number of cases. The performance measurements further confirmed using ANOVA (analysis of variance) to test the level of statistical significance of the differences in classifier accuracy. This critical work has greatly bolstered the dependability and accuracy at which decisions in agriculture are made. The sowing recommendation is conditioned in this way before the predicted values, increasing the accuracy of predictions and supplying, through validation, a comprehensive framework as a basis for future studies in predictive modeling and classification in agriculture sets.