Abstract
Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
Highlights
In cucumber production, a high consumption of nitrogen occurs more often than a low consumption
The Most Effective Wavelengths for the Classification of Cucumber Leaves Based on Nitrogen Content
Investigation of the Performance of Different Classifiers for the Classification of Cucumber Leaves Based on Nitrogen Content in 200 Iterations
Summary
A high consumption of nitrogen occurs more often than a low consumption. Balance in the consumption of nitrogen is very important to reduce nitrate accumulation in green cucumber. The most commonly objective methods are chemical analytical methods such as mass spectrometry (MS) and high-performance liquid chromatography (HPLC). They have several disadvantages, including being destructive, time-consuming, and costly. Accurate, reliable, efficient, and non-destructive options are strongly needed to evaluate the quality-related characteristics of food products (Salimi et al [2]; Pourdarbani et al [3]; Sabzi et al [4])
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