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.
