Treffer: Enhancing Crop Yield Prediction Through Image-Driven Multi-Level Feature Learning and Regularized SNN-GRU.

A Regularized Spiking Neural Network and Gated Recurrent Unit (RSNN-GRU) with multi-level feature learning are used in the proposed model to forecast agricultural yield. Using t-SNE and Kernel PCA approaches, the preprocessing step encompasses data cleaning, standardization, normalization, and dimensionality reduction. The dataset is divided into training and testing sets with an 80-20 split to create the model. Statistical and higher-order statistical features as well as two newly proposed features, adaptive weighted kurtosis, and adaptive weighted skewness, are all used in feature extraction. A Self-Adaptive Farmland Fertility Optimization (SAFFO) algorithm is used for feature selection, improving feature selection performance. The SAFFO algorithm's hyperparameter adjustment helps the RSNN-GRU model even more. The performance of the proposed model is compared to that of previous deep-learning models for agricultural yield prediction using some evaluation metrics. The proposed model performs better than others, according to the results, and has a lot of potential for usage in the agricultural sector. Python 3.7.9 is used to carry out the proposed model's implementation. The results show that the proposed SNN-GRU has better performance in terms of predicting the better crop-yield with an accuracy of 96.1% and 96.5% with the help of hyperparameter learning rate of 0.7 and 0.8 respectively. The other metrics such as precision, sensitivity, specificity and F-measure have shown better results when compared to existing models such as CNN, RNN, LSTM and DNN. [ABSTRACT FROM AUTHOR]

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