Large-scale agricultural production systems require disease monitoring and pest management on a real-time basis. Monitoring disease phenology is one of the possible ways to save agricultural products from huge yield loss incurred due to diseases. Rice is one of the major food crops across the globe. Leaf blast disease in rice affects its productivity all over the world. Monitoring of leaf blast is essential for strategic and tactical disease management decisions. Conventional methods of large-scale disease monitoring are laborious, time taking, and above all, suffer from inaccuracy. Remote sensing parameters are useful for monitoring diseases and crop health on a large scale. Spectral indices derived from remote sensing data provide characteristic features to distinguish areas between healthy and infected crops facilitating strategic application. Assessment of leaf blast incidence based on land surface temperature (MODIS) and spectral indices NDVI, EVI, NDMI, SAVI, and moisture stress (Sentinel-2) have been used to predict disease patterns. A deep learning-based model is developed to assess the condition of rice blast disease at field scale. The model provided 90.02% training accuracy and 85.33% validation accuracy. The deep learning model on remote sensing images could assess leaf blast occurrence in real time.

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system.Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer’s field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features.Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model.Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.