Analysis Of Classifiers With Different Attributes Related With Agriculture Patterns Using Deep Learning

The increasing demand for sustainable and efficient agricultural practices has accelerated the integration of artificial intelligence (AI), particularly deep learning, into modern farming systems. This study presents a comprehensive analysis of various deep learning classifiers—specifically CNN-based architectures—applied to key agricultural tasks, including crop classification, yield prediction, and harvest timing. Using both open-source and field-collected datasets encompassing image, climatic, and soil data, multiple models such as VGG16, ResNet50, InceptionV3, and a hybrid CNN+LSTM were evaluated for their effectiveness. The CNN+LSTM model consistently demonstrated superior performance, achieving the highest accuracy in classification and harvest timing, and the lowest error in yield estimation. These findings underscore the importance of combining spatial imagery with temporal environmental inputs for precision farming applications. The study also explores the practical relevance of deploying these models in real-world smart agriculture systems, while emphasizing future directions involving explainable AI, multimodal data fusion, and mobile-edge deployment for broader accessibility. Overall, this research contributes to the growing field of AI-driven agriculture by identifying robust, scalable models capable of enhancing decision-making processes across diverse agro-ecological settings.