Vegetable Yield Prediction and Fertilizer Recommendation Using Optimized PINN and Independent Shearlet Based DBN Approach

Abstract

Accurate vegetable yield prediction and precise fertilizer recommendations are crucial for maximizing agricultural productivity and sustainability. Advanced methodologies, including machine learning algorithms and precision agriculture tools, offer significant improvements in forecasting crop yields and optimizing nutrient application. However, traditional models often depend on extensive, high-quality datasets, which may be challenging to obtain in less-developed regions. Moreover, traditional fertilizer recommendation systems may not sufficiently adapt to real-time changes in soil conditions or crop requirements, leading to less precise nutrient management. In order to address the aforementioned problems, vegetable yield prediction and fertilizer recommendations are made using optimal machine learning and hybrid deep learning models. In this paper, the developed model collects agricultural data from a standard source. Subsequently, the collected data undergoes three pre-processing techniques to improve crop yield prediction. Data cleaning involves identifying missing or incomplete values, while data normalization ensures all features contribute equally to model training using weighted k-means and Neighbourhood averaging addresses outliers. After that pre-processed data is used for feature selection, using Relief Feature Ranking with Recursive Feature Elimination. The selected data is used for crop yield prediction and fertilizer recommendation. Physics-informed neural networks (PINN) based fruit fly optimization (IFO) algorithm is employed for predicting the yield of various vegetables like chickpeas, kidney beans, blackgram, lentil, etc. A hybrid Independent Shearlet-based Deep Belief Network (IS-DBN) is used for fertilizer recommendation. The performance metrics for vegetable prediction and fertilizer recommendation attained for the proposed model are 99.17 and 96.99% of accuracy, 91.67 and 89.65% of precision. The proposed model’s obtained values are better than those of the existing methods. Thus, the proposed optimized machine learning and hybrid deep learning approach effectively predict crop yield and fertilizer recommendation with higher accuracy.