Review of AI-Driven Machine Learning Solutions for Modern Agriculture

Artificial intelligence (AI) has been more prevalent in the agricultural sector in recent years. Sector challenges, there are many obstacles to overcome to increase the crop production namely reduced soil preparation, pest invasion Insufficient data leading to wrong result prediction hence the poor output, and wide breach between farmers and evolving technologies. The proposal of applying AI techniques in agriculture is to ensure its flexibility, precision, reliability, and goodness. The study summarizes the many AI-based approaches to weed, disease, crop, and soil management solutions. Several approaches are used to increase the yield, and the benefits and drawbacks of the application are examined. To produce high yields of fruit, plants must have a perfect balance between vegetative development (such as root, stem, and leaf growth), reproductive growth (such as flower and fruit growth), NNs, and GA. This allows for the best nutrient concentration (NC) set points. One way to forecast plant growth is to look at the ratio of stem diameter to total leaf length.