Combining AI and Big Data for Precision Agriculture in the Life Sciences Sector

Abstract

Precision agriculture using genetic, phenomic, AI, and big data analytics will boost output, resilience, and sustainability. Technological convergence allows us use data to better agriculture and the environment. The ease of obtaining massive biological data has changed agriculture. Big data and AI predict complicated biological phenomena. This provides accurate crop management. 

Precision farming increases yields, reduces waste, and promotes sustainability. AI can analyse and exploit massive agricultural data. This strategy manages complex genetic, phenomic, environmental, and agronomic interactions using big data, machine learning, and deep learning. AI may use satellite, soil, meteorological, and genetic data. This lets you provide precise crop and weather advice. 

AI biology targets agricultural genomics and phenomics. Genomic order is needed to understand disease resistance, drought tolerance, and yield. Genomic sequencing yields vast data. AI analysis of these data sets indicates biological mechanisms affecting traits. However, phenomics describes phenotypes. Plant traits include height, leaf area, and stress sensitivity. AI systems can relate genetics to physical attributes using phenomic and genomic data. Finding plant genes that affect desired features is straightforward. 

Precision farming using AI and big data boosts yields. Food security requires predicting plant and animal reactions to climate change, pests, and diseases. Historical weather, soil health, and environmental data may assist AI forecast crop damage. AI-based genetic selection may produce stress-resistant crops. Breeders may use models to choose parent plants with complementary traits to generate cultivars that thrive in varied circumstances. 

The AI optimises agricultural resources. AI models can use massive sensor, satellite, and IoT data to advise on water and nutrient management. Waste falls and operations improve. Based on soil moisture, weather, and crop water demands, AI algorithms may irrigate. To optimise fertilisation, AI monitors soil nutrients and crop development. Farmers can maximise yield with less fertiliser. 

Precision agriculture may employ AI to manage pests and diseases. Agricultural equipment, sensors, drones, and others can provide big data for pest and plant disease prediction. AI can swiftly identify data trends. Farmers may reduce chemicals and prevent pests. Reduces farming's environmental impact and improves pest control. 

AI and big data impact agricultural gene development. AI systems can find behavior-related genetic variations in large genomes, phenomics, and environmental data. In order to speed up breeding, scientists may chose plants with high yield, disease resistance, or climate adaptation genes. AI-powered genomic selection methods like genomic prediction may help breeders forecast their children's performance, boosting crop growth. 

Precision agriculture may change with AI and big data, but many issues remain. Genetic and agricultural data must be private. AI models need massive processing power and data infrastructure, making them difficult to deploy in developing countries. More standard mechanisms are needed to share agricultural data across sectors and stakeholders. 

Precision agriculture employing AI and big data analytics might alter life sciences by helping farmers grow more productive, robust, and long-lasting crops. AI models can improve crop breeding, pest control, resource optimisation, and environmental adaption utilising agricultural genomics and phenomics data. Global food security may improve with agricultural innovations. This will help farmers feed a growing population while protecting the environment.