The coconut tree (Cocos nucifera) is one of the most extensively and widely used palm trees. The coconut palm is regarded as the "tree of life," or "Kalpavriksha." Humans have been cultivating a wide range of coconut trees around the world. This study is based on analysis of secondary data on cultivation of coconut trees and its benefits. Due to increased insect infestations and a changing habitat, the lifespan of coconut trees have been affected. Coconut product market has become the fastest expanding business due to the anti-viral compounds found in it. The coconut is used to produce oils, even the shells used for craft materials. People have been using coconut trees to make brooms and woods are being used to make furniture, construction materials for dwellings, and hardwood floors. It shows the livelihood of people around the world have been changed due to the cultivation of coconut trees. Indonesia, India and Philippines are top three countries to be economically benefitted from the plantation of coconut trees. As there is high demand of coconut based products worldwide, farmers need to be made aware about this economic value for increment in production.

Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the “wet” season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test.