Artificial Intelligence-genomic Studies in The Advancement of Agriculture

Artificial Intelligence in agriculture biology plays a vital role in the improvisation of crop production and enhances resistance against plant pathogens. Artificial intelligence brings about changes in crop production by predicting the gene data, showing the ability of plants to resist plant pathogens and environmental conditions. Machine learning methods, namely artificial, neural, and Deep Neural networks. Computational approaches were used to determine Plant Genomics. The main aim of this review study was to understand plant genomics data, predict plant genomes based on machine learning and reduce the cost of fertilizers and side effects. The seven important factors include soil moisture, the electric conductivity of soil solution, evapotranspiration, humidity, soil aeriation, and soil pH and air temperature. The red, green, and infrared channels of sensors in three layers of ANN were used for the determination of genomic data. Chemical fertilizers are used to kill pests damaging crops and affecting the ecosystem. Farmers and agricultural scientists are looking forward to implementing advanced machine learning techniques such as sensors mounted on vegetable and fruit orchards. The traps were manufactured and installed by using sensors to detect parasites infecting crops of agricultural importance. This review study was focused on computational data on plant genomics and promoting less usage of fertilizers to prevent carcinogenic and genomic diseases. The researchers performed an experiment and stated that eight master transcription factors are the most vital to enhance the ability to fix nitrogen from the atmosphere. Farmers are future artificial intelligence Engineers. Based on the review of the literature, it was evident that artificial intelligence enhances crop improvement for better productivity.

Keywords: Artificial intelligence, ANN, CI, CNN, DNN, Gene editing, Machine learning, ML Algorithms, Plant Genomics, Sensors.