A Conceptual Framework for Smart Agriculture Management using Convolutional Autoencoder with Long Short-Term Memory

Recently, smart agricultural farming is emerging with high improvements in practical applications, making it a very significant valuable, and important device. The identification of plant disorder is one of the significant aspects of managing an agriculturally implemented nation. The effective and timely identification of plant disorders is significant for a productive and healthy agricultural domain and eliminates wasting money and other significant resources. The protection of crops plays an important role in enhancing requirements for food quantity and quality. The traditional crop yield prediction demands plant acreages along with sample validations that require plant-cutting research. Because of the poor learning ability of conventional machine learning-aided irrigation techniques, these models are not applicable to unpredictable climates. Hence, a new smart agriculture management model is presented using deep learning to perform crop yield detection, irrigation, crop detection, and disease detection. Initially, the data and images needed for smart agriculture management are gathered from benchmark sites. At first, the collected data is utilized to perform crop yield detection and irrigation via the developed Convolutional Autoencoder with Long Short-Term Memory (CAE-LSTM). This process is helpful to decrease the water usage in crop fields and increase the crop production. Then, the accumulated images are utilized by the proposed CAE-LSTM for performing crop detection and crop disease detection. The proposed deep learning-assisted smart agriculture management approach is useful in diagnosing crop disease at its early stages. At last, the superiority of the suggested system is evaluated with existing techniques to analyze its performance.