Quantum Computing (QC) introduces a new methodology in data processing by employing quantum mechanics to improve computational efficiency. This study explores the application of the Pegasos Quantum Support Vector Classifier (PQ-SVC) and Quantum Neural Network (QNN) for agricultural data analysis, focusing on crop classification and prediction. PQ-SVC is an optimized quantum variant of the traditional Support Vector Machine (SVM) which leverages quantum kernels to improve classification accuracy. QNN utilizes quantum circuits to capture complex data patterns. In this work, crop prediction is carried out using PQ-SVC and QNN. Experimental evaluations reveal that PQ-SVC achieves an accuracy of 88%, whereas QNN attains 70%. The use of quantum feature encoding and circuit-based optimization contributes to the effectiveness of these models. These results emphasize the promise of quantum algorithms in enhancing predictive analytics and provides more precise decision-making in the agriculture sector.