a big data-driven agricultural system for remote biosensing applications

Objectivethis study explores two big data (bd)-driven agricultural models backed by the national institute of food and agriculture (nifa). it examines the benefits of thorough agricultural records, efficient phenotyping techniques, and teamwork in promoting agriculture, highlighting the role of technological advancements like sensors, robotics, machine learning (ml), big data analytics, remote sensing, and genomics in addressing global food and water security issues. the study’s primary goals were to examine the advantages of maintaining accurate agricultural records for better breeding and agronomy, investigate strategies for efficient phenotyping and data collection in agricultural systems, then support interaction between plant breeders, agricultural scientists, and specialists in ml, remote biosensing (rbs), and bd, and to determine the financial requirements for the ongoing advancement of bd-driven agriculture models.materials and methodsthe aviris indian pines database was utilized in these tests. the indian pines database encompasses the farming industry. the dataset consists of 16 different groups. the studies used an intel i5 laptop with a 2.4-ghz central processing unit (cpu) (four cores) and 16 gigabytes of memory.resultsthe integration of technology, including sensors, remote sensing, robots, and bd analytics, enhances high-throughput phenotyping and precision farming. multidisciplinary cooperation accelerates crop breeding and management. future financing is needed for predictive machine learning models and scalable phenotyping techniques.conclusionsbd analytics, remote sensing, and machine learning have the ability to transform agronomy and plant breeding, tackling issues related to food security. sustained cooperation and sufficient infrastructure investment are essential for successful implementation, with specific funding required for the development of cutting-edge instruments and technologies that guarantee sustainable farming methods..

a big data-driven agricultural system for remote biosensing applications

objectivethis study explores two big data (bd)-driven agricultural models backed by the national institute of food and agriculture (nifa). it examines the benefits of thorough agricultural records, efficient phenotyping techniques, and teamwork in promoting agriculture, highlighting the role of technological advancements like sensors, robotics, machine learning (ml), big data analytics, remote sensing, and genomics in addressing global food and water security issues. the study’s primary goals were to examine the advantages of maintaining accurate agricultural records for better breeding and agronomy, investigate strategies for efficient phenotyping and data collection in agricultural systems, then support interaction between plant breeders, agricultural scientists, and specialists in ml, remote biosensing (rbs), and bd, and to determine the financial requirements for the ongoing advancement of bd-driven agriculture models.materials and methodsthe aviris indian pines database was utilized in these tests. the indian pines database encompasses the farming industry. the dataset consists of 16 different groups. the studies used an intel i5 laptop with a 2.4-ghz central processing unit (cpu) (four cores) and 16 gigabytes of memory.resultsthe integration of technology, including sensors, remote sensing, robots, and bd analytics, enhances high-throughput phenotyping and precision farming. multidisciplinary cooperation accelerates crop breeding and management. future financing is needed for predictive machine learning models and scalable phenotyping techniques.conclusionsbd analytics, remote sensing, and machine learning have the ability to transform agronomy and plant breeding, tackling issues related to food security. sustained cooperation and sufficient infrastructure investment are essential for successful implementation, with specific funding required for the development of cutting-edge instruments and technologies that guarantee sustainable farming methods..