Distinguishing Agro - Based Impediments Using DL System and Outlier Integration - Arpana Prasad, N Krishnamoorthy, C. Gnana Kousalya, Palamakula Ramesh babu, Satyajit Sidheshwar Uparkar, R. Thiagarajan (Mathematical Statistician and Engineering Applications, 2021)
Smart agriculture is being implemented to minimise farmers’ time consumption and make it more cost-effective. The autoencoder in smart farming uses a homogeneous set of features to detect anomalies within agricultural fields. Anomaly detection with deep learning establishes smart farming, where it predicts the vast amount of data. Machine learning extracts the massive amount of context information. As the population increases, food becomes scarce. A high level of agricultural production is sustained with a regression algorithm that improves diversification. To determine the complexity of the behavior, the pattern type must not exhibit the expected set of behaviours. To detect the aberrant set of behaviours, anomaly detection is used. Monitor all the agricultural data and livestock regions using different collection procedures.