A PROPOSED MODEL FOR PREDICTING THE MATURITY OF GROUNDNUT

The use of information communication Technology (ICT) has grown exponentially that its monumental application can be seen in almost every aspect of human endeavor of which agriculture is not an exception to the profound benefits provided by this field. Groundnut is used to be one of the most remunerative farming enterprises in Nigeria prior to the discovery of crude oil. In Nigeria groundnut is crushed to produce roasted snacks, groundnut oil or boiled either in the shell or unshelled for direct consumption. This research work aims at mitigating the difficulty associated with manual detection of groundnut maturity using certain features of the leaves. Customary, to examine the maturity of groundnut it requires constant monitoring and observation of changes in the color of groundnut leaves from purely green to purely yellow. This method of maturity assessment in order to harvest the crop without excessive loss is less accurate and consumes an awful lot of time particularly in a large farm. Hence, this approach cannot be fully reliable as color is subjective to our naked eyes and failure to harvest the crop when it reaches optimum maturity stage might cause the seeds pod to decay/ germinate underground due to moisture which might eventually result in quantity reduction of the expected yield. Consequently, design of an automated system is pivotal to farming and becomes necessary in the context of ICT era. This task is achieved by identifying the stages of the leaves of the groundnut plant using a convolutional neural network classifier. An