FUZZY LOGIC MODEL FOR PREDICTING PEANUT MATURITY

Abstract

Peanut quality and yield are impacted by harvest timing. The most commonly used tool for determiningharvest timing is the hull-scrape chart giving association of kernel maturity to color of mesocarp. The hull-scrapetechnique is tedious, time-consuming, and labor-intensive. Wider use of maturity evaluations would be greatly facilitatedby a quicker and easier test. While testing for maturity, the NMR signals from peanuts and days after planting exhibit anonlinear relationship with the maturity class of kernels. Therefore, linear classification techniques such as lineardiscriminant analysis (LDA) may not achieve good classification results. This article describes the development of afuzzy model to predict peanut maturity based on NMR-signal (FIDPK) and days after planting (DAP). Compared to thehull-scrape method, the fuzzy model predictions were 45%, 63%, and 73% accurate when maturity was classified in6 classes, 5 classes and 3 classes, respectively. The respective accuracies from LDA, using the same data, were 42%, 56%and 70%. Data from 346 kernels were used for performance evaluation of both the fuzzy and LDA models. The fuzzymodel improved maturity prediction compared to LDA. These results are encouraging, however, fuzzy model should befurther evaluated with new data.