Enhanced crop health monitoring: attention convolutional stacked recurrent networks and binary Kepler search for early detection of paddy crop issues.

The diseases that affect the plants cannot be easily avoided due to rapid and substantial changes in the environment and climate. Generally, paddy crops are affected by several conditions including pests and nutritional deficiencies. Hence, it is important to detect these disease-affected paddy crops at an early stage for better productivity. To detect and classify the problems in this specific domain, deep learning approaches are utilized. In this paper, a novel attention convolutional stacked recurrent based binary Kepler search (ACSR-BKS) algorithm is used to detect diseases, nutritional deficiencies, and pest patterns at an early stage via diverse significant pipelines namely the data augmentation, data pre-processing, and classification phase thereby providing pest patterns and identifying nutritional deficiencies. Subsequent to data collection processes, the images are augmented via zooming, rotating, flipping horizontally, shifting of height, width, and rescaling. To acquire the accurate and best results in terms of classification, the parameters need to be tuned and adjusted using the binary Kepler search algorithm. The results revealed that the accuracy of the proposed ACSR-BKS algorithm is 98.2% in terms of detecting the diseases. Then, the obtained results are compared with the other existing approaches. Additionally, it is revealed that the yield of paddy can also be improved by utilizing the proposed disease-detecting methods.