In this study, we present an innovative approach for onion crop monitoring utilizing multispectral imagery and leveraging the power of Convolutional Neural Networks (CNNs) in deep learning. The objective is to develop a robust model capable of accurately recognizing different growth stages of onion crops through the analysis of captured datasets comprising multispectral images. Our proposed method involves the implementation of a progressive CNN architecture, finely tuned to discern intricate details in the onion crop development process. By harnessing the inherent capabilities of CNNs, we aim to achieve a higher level of precision in identifying key growth stages, facilitating efficient crop management and monitoring. The model is designed to adapt and learn from the diverse spectral information inherent in multispectral imagery, providing a holistic understanding of the onion crop's developmental dynamics. Through the integration of deep learning techniques, our approach seeks to enhance the accuracy and efficiency of onion crop growth stage recognition, contributing to the advancement of precision agriculture and sustainable farming practices. Keywords: Multispectral ,CNN, Precision, Monitoring and Deep Learnin