Fuzzy logic model for predicting peanut maturity

Peanut quality and yield are impacted by harvest timing. The most commonly used tool for determining harvest timing is-the hull-scrape chart giving association of kernel maturity to color of mesocarp. The hull-scrape technique is tedious, time-consuming, and labor-intensive. Wider use of maturity evaluations would be greatly facilitated by a quicker and easier test. While testing for maturity, the NMR signals from peanuts and days after planting exhibit a nonlinear relationship with the maturity class of kernels. Therefore, linear classification techniques such as linear discriminant analysis (LDA) may not achieve "good" classification results. This article describes the development of a fuzzy model to predict peanut maturity based on NMR-signal (FIDPK) and days after planting (DAP). Compared to the hull-scrape method, the fuzzy model predictions were 45%, 63%, and 73% accurate when maturity was classified in 6 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 fuzzy model improved maturity prediction compared to LDA. These results are encouraging, however; fuzzy model should be further evaluated with new data.