Detection of Underdeveloped Hazelnuts from Fully Developed Nuts by Impact Acoustics

Abstract

Shell-to-kernel weight ratio is a vital measurement of quality in hazelnuts as it helps to identify nuts that have underdeveloped kernels. Nuts containing underdeveloped kernels may contain mycotoxin-producing molds, which are linked to cancer and are heavily regulated in international trade. A prototype system was set up to detect underdeveloped hazelnuts by dropping them onto a steel plate and recording the acoustic signal that was generated when a kernel hit the plate. A feature vector comprising line spectral frequencies and time-domain maxima that describes both the time and frequency nature of the impact sound was extracted from each sound signal and used to classify each nut by a support-vector machine. Experimental studies demonstrated accuracies as high as 97% in classifying hazelnuts with underdeveloped kernels.

Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

“…The results of Pearson et al (2005), Kalkan and Yardimci (2006), and Onaran et al (2006) emphasized the importance of signal processing methods for the impact acoustic signal to achieve higher accuracies in food kernel inspection. These improvements were obtained by extracting frequency and time domain features separately.…”

“…Although these feature combinations provided good results for IDK separation, the authors also reported poor results on other types of kernel damage, such as scab and infested kernels at the pupal stage, where the insect was still inside the kernel. The same impact acoustics based system was also recently extended to separate cracked hazelnut shells from undamaged ones (Kalkan and Yardimci, 2006) and immature hazelnuts from fully developed hazelnuts (Onaran et al, 2006). Specifically, Kalkan and Yardimci (2006) reported a classification accuracy of 91.8% in cracked and healthy hazelnut shell separation by using the energy in the frequency subbands of impact acoustics.…”

Advances in Artificial Neural Networks – Methodological Development and Application

“…The figure indicates that although the number of peer reviewed publications on this topic did not increase steadily over the past few years, a trend exists for total number of papers and report to increase in the past few years and to be able to project this trend in the next few years. The successful implementations of SVMs in agricultural and biological engineering are listed in Table 2, which includes classification of intact and cracked eggs [122], classification of forest data cover types [123], classification of meat with small data set [124], black walnut shell and meat classification using hyperspectral fluorescence imaging [125], classification to differentiate individual fungal infected and healthy wheat kernels [126], classification for weed and nitrogen stress detection in corn [127], discrimination for screening of compound feeds using NIR hyperspectral data [128], identification of tea varieties by computer vision [129], discrimination of wheat classes with NIR spectroscopy [130], detection of underdeveloped hazenuts from fully developed nuts by impact acoustics [131], classification of electronic nose data [132], classification of modified starches [133], classification of milk with an electronic nose [134], and recognition of plant disease [135]. In these applications, Zhang et al [126] fused SVM with kernel of RBF neural network.…”

System for removing shell pieces from hazelnut kernels using impact vibration analysis

“…As a result, it is possible to design a system for removing shell fragments and pieces from hazelnuts by analyzing the impact signals. Impact sound and vibration analysis systems have been widely used in practice Haff and Pearson (2007), Yorulmaz et al (2012Yorulmaz et al ( , 2011, Pearson et al (2007b), Cetin et al (2004), Onaran et al (2005), Pearson et al (2005Pearson et al ( , 2007a, Cataltepe et al (2005Cataltepe et al ( , 2004b, Cetin et al (2005), Ince et al (2008), Buerano et al (2012), Omid et al (2010), Chen et al (2011) and Michihiro and Takahisa (2012). Haff and Pearson developed a sorting system to separate pistachio kernels from in-shell nuts using vibration analysis of a small steel plate after a shell or nut piece impacted it Haff and Pearson (2007).…”