Agricultural bioinformatics and machine learning techniques in Areca nut disease and crop improvement – a review

Abstract

Bioinformatics is an interdisciplinary scientific field comprising biology, mathematics, and computer science. It is the application of information technology to the management of biological data that aids in deciphering plant genomes. Biological research, which previously began in laboratories, fields, and botanical clinics, now begins at the computational level, using computers (in silico) for data analysis, experimental design, and hypothesis development. Agriculture is the backbone of the nation, so agricultural bioinformatics is one of the fastest-growing scientific fields that use computational approaches to study biology and life sciences. India ranks first in the world for areca nut production and many farmers depend on areca nut cultivation for their livelihood. Areca nut yields are affected by many diseases caused by heavy rainfall and high relative humidity. Early prediction of crop diseases based on weather data helps farmers take preventive measures. Many machine learning methods are used to detect a disease from image data. Expanding knowledge about the molecules and mechanisms associated with specific phenotypic traits and specific responses to biotic or abiotic stresses will be complemented by the predictive power of bioinformatics to influence agricultural practices and improve diagnostic, monitoring, and advocating innovative methods in traceability, enhancing human value and supporting sustainability at low cost. This review briefs about the field of agricultural bioinformatics and the application of machine-learning techniques in the overall crop improvement of areca nut including disease prediction.