Abstract

In India, agriculture plays a crucial role in feeding the population and boosting the country's gross domestic product. Farmers often face challenges in choosing the right crop based on soil conditions, which can greatly influence the success of their cultivation. By selecting the most suitable crop, farmers can maximise yield potential, leading to increased economic profitability. Precision agriculture (PA) utilizes machine learning (ML) techniques to address challenges in agriculture. The concept of PA enables using advanced technologies and data-driven approaches to effectively enhance agricultural production. A model for crop recommendation was developed using a deep residual network (DRN) optimized by the chronological sparrow search algorithm (CSSA). Initially, the input dataset undergoes data preprocessing, including mean imputation to handle missing values and Z-score normalization to standardize feature values. This process enhances model performance by accounting for various factors such as , content in the soil, as well as temperature and rainfall. Subsequently, the deep maxout network (DMN) with Canberra similarity performs feature fusion to reduce the dimensionality of data. The DRN is train the model using the CSSA, which optimizes hyperparameters to improve the model's performance. The CSSA–DRN model was observed to outperform other models. The proposed model achieved a precision of 85.4%, a recall of 87.5%, an F-measure of 81.3%, and an accuracy of 92.7%. The CSSA-DRN enhances crop recommendations, maximizing yield and boosting profitability.

Keywords

Crop recommendation, Precision agriculture, Chronological sparrow search algorithm, Deep residual network, Machine learning.

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