Intelligent Crop Yield Level Prediction Using a Zebra Optimization-Based Multilayer Perceptron Prediction Mechanism (ZMPPM)

K.Venkata Rama Rao; Dr Lakshmi Devi N; Dr. A. Mahendar; Chavvakula Janaki Devi; Jagan Mohan Sarihaddu; Dr. V. Gokula Krishnan

doi:10.51278/ajse.vi.2307

Authors

K.Venkata Rama Rao Department of CSE, Malla Reddy College of Engineering and Technology, Hyderabad 500100, India
Dr Lakshmi Devi N Department of CSE-AIML, GMR Institute of Technology, Rajam, India
Dr. A. Mahendar Department of CSE (Data Science), CMR Technical Campus, Kandlakoya, Hyderabad, Telangana 501401, India
Chavvakula Janaki Devi Department of Electronics & Communication Engineering, Aditya University, Surampalem, Andhra Pradesh 533437, India
Jagan Mohan Sarihaddu Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India
Dr. V. Gokula Krishnan Department of CSE, Easwari Engineering College, Ramapuram, Chennai 600089, Tamil Nadu, India

DOI:

https://doi.org/10.51278/ajse.vi.2307

Abstract

Accurate yield prediction has a significant impact on the overall improvement of agricultural planning better resource management, and even food security. Here a new Zebra Multilayer Perceptron Prediction Mechanism (ZMPPM) is presented as a method of realizing effective yield prediction from historical and environmental data. The framework comprises four stages: data preprocessing, Zebra Optimization Algorithm (ZOA)-based feature selection, ZOA-assisted training of a multilayer perceptron (MLP), and classification of crop yield into Low and High yield levels. In ZMPPM, the ZOA is used (i) to select the most informative subset of soil, climatic and management variables and (ii) to optimise the learning parameters of the MLP, which then captures the nonlinear relationships between these variables and yield. The model was evaluated on a historical crop yield dataset of soil, climatic and management variables using a stratified hold-out test set of 5,000 records. On the test set, ZMPPM achieved an accuracy of 99.07%, a precision of 99.40%, a recall of 99.30% and an F1-score of 99.35%. Compared with Decision Tree, Gradient Boosting Machine, XGBoost, LSTM and CNN-LSTM baselines, ZMPPM improved accuracy by 1.50 percentage points over the best baseline (LSTM, 97.57%). These results indicate that ZMPPM is a promising component for agricultural decision-support systems, although validation on additional regions and seasons is still required.

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