An Approach for Detecting Complications in Agriculture Using Deep Learning and Anomaly-Based Diagnosis

Abstract

Deep learning employs the homogeneous of features to identify anomalies using outlier detection. To minimise the farmers effort along with cost efficient, smart farming is implemented. Since, large amount of data are difficult to handle, so to improvise the machine learning is used. Large data derives out the techniques is implemented. The regression algorithm plays a vital role in producing the diversity by maintaining high level of productivity. To increase the overall nutritional quality smart farming is employed. The high resistance of crops against diseases and catastrophic events decreases as crop varieties deteriorate. The complex set of patterns does not have proper set of expected set of behaviour. The deep neural network used in this study demonstrates its feasibility due to the high accuracy of the deep model on field image order. To detect and categorize the abnormal set of behaviour anomaly detection is utilized. State-of-the-art approaches in agriculture and animal husbandry use this process to monitor all data. In this research, the complications in agriculture are detected and monitored using deep learning with anomaly-based diagnosis.