ABSTRACT
The crop cutting experiment (CCE) is an efficient assessment method used to determine the estimated yield that can be expected in that agricultural cycle. Earlier methods consisted of random sampling of areas where various soil and crop cycle factors were tested to indicate harvest criteria over a larger area. With the help of artificial intelligence and machine learning, digital solutions have been introduced in CCE to help bring focused results.
These digital solutions, SmartFarm and SmartRisk, use ground-level data and satellite imagery to identify the plots that are appropriate for these experiments. A dedicated and highly skilled data science team analyses millions of data points. It runs them through numerous criteria to zero in on farm plots that will provide the most accurate sample for the region. In this way, ambiguity is removed from plot sampling, making crop insights value rich and focused.
