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High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods are crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination of a set of exemplars. In this study, we propose a local sparse coding framework within the context of a classification problem. The objective is to predict the label of a given data point based on labeled training data. The primary optimization problem encourages the representation of each data point using nearby exemplars. We leverage the optimized sparse representation coefficients to predict the label of a test data point by assessing its similarity to the sparse representations of the training data. The proposed framework is computationally efficient and provides interpretable sparse representations. To illustrate the practicality of our proposed framework, we apply it to agriculture for the classification of crop diseases.