Precision Agriculture and Its Future

Natural resources, biotic variables, agro-inputs, and management all impact agricultural production. Uncontrolled use of resources and inputs frequently occurs by farmers, which results in environmental pollution, degradation of land, and financial loss to farmers. The term "precision farming" describes the integration of GIS and GPS tools to provide extensive detailed information on crop growth, crop health, crop yield, water absorption, nutrient levels, topography, and soil variability [1]. Precision agriculture makes use of technology such as sensors, GPS, GIS, Internet of Things, drones, etc., among other things, to optimize the use of natural resources and farm inputs for a given crop production and quality. Agriculture could become more productive and consistent due to digital agriculture and more effective use of resources and time. This article presents the gist of Precision Agriculture along with its components and future implications

Diversity Analysis of Rhizoctonia solani Kuhn Isolates Causing Sheath Blight of Rice in Eastern Uttar Pradesh, India

The sheath blight of rice is a devastating fungal disease, which is caused by Rhizoctonia solani AG1 IA. Twenty-one sheath blight isolates of rice were collected from different locations in eastern Uttar Pradesh, India, to study the variation in cultural, pathogenic, and molecular characterization. All the R. solani isolates were classified into four groups depending on the arrangement of sclerotia, i.e., peripheral, centre, scattered, and last group includes placing at the centre and peripheral 11, 1, 5, and 4 isolates, respectively. Depending on pathogenicity, isolates are grouped into weakly virulent, virulent, and highly virulent, representing 12, 4, and 5 isolates, respectively. The random amplified polymorphic DNA has been successfully used for molecular characterization. In our study, ITS1/ITS4 and AG-1 IA-specific markers yielded reproducible banding patterns. UPGMA cluster analysis revealed two major clusters, A and B, with a 13.85% dissimilarity value. This study does not correlate with virulence, geographical location, and RAPD profile groupings