Mass detection of walnut based on X‐ray imaging technology

Abstract

In view of the problem of low accuracy in walnut mass detection caused by relatively inconstant density, this study suggested the integration of X‐ray imaging technology and image processing technology with machine learning for walnut mass detection. Using image processing technology to remove the background of walnut X‐ray image and to segment the kernels, the mass prediction models could be constructed after extracting the shape and texture of walnut characteristic parameters and the kernel shape characteristic parameters. The results revealed that when mass prediction models were built based on walnut shape characteristic parameters or constructed with the application of texture characteristic parameters, the determination coefficient (R2) were rather low, even the residual predictive deviation (RPD) less than 1.4, proving that these models were not reliable. However, when mass prediction models were built based on walnut and kernel shape characteristic parameters, the R2 for both partial least squares (PLS) and radial basis function (RBF) models were higher than 0.84. And the RPD values were 1.8133 and 1.7474, respectively. On this basis, when competitive adaptive reweighed sampling (CARS) optimized parameters were adopted, the R2 for both PLS and RBF models were higher than 0.86 and the RPD were 1.8759 and 1.8850, respectively. The result proved that the application of walnut and kernel shape characteristics can improve the accuracy of model prediction. Therefore, using X‐ray imaging technology to detect walnut mass was feasible, which could realized the prompt, accurate and nondestructive detection of walnut mass. This technology provided a novel thought for the accurate grading of walnuts. Practical Applications At present, there is a problem of similar size but large mass difference after walnut size classification, which directly affects the commodity value of walnuts. It is necessary to combine multiple characteristics to select and classify walnuts. Mass is an important grading index. With the development of machine vision technology, online detection of fruit mass has been realized. However, due to the relatively inconstant density of walnuts and the limitation of the technology, the machine vision technique cannot obtain internal information. This study used X‐ray transmission to obtain the internal information of walnuts without destroying the walnuts. At the same time, the mass detection models were established by combining the internal and external characteristics of the walnuts to achieve fast, accurate, and nondestructive testing of walnut mass. Automatic mass detection provides technical support for intelligent grading, which has important production significance and economic value for the development of the walnut industry.

The shape and volume of air, kernels and cracks, in a nutshell

“…In particular, they observe that larger fruits are correlated with rougher shell shape and smaller kernel filling ratio, which allows them to select for better genotypes. X-ray CT imaging has also been recently used to document morphological changes of walnut flower bud development (Gao, 2022), estimate kernel weight in an nondestructive way (Gao et al, 2022), and to explore the puzzling diversity and structure of the cell tesselations that conform the hard shell tissue for multiple nuts (Huss et al, 2020).…”

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

“…Therefore, it is necessary to develop a non-destructive and rapid method for identifying maize seed varieties. Near-infrared spectroscopy and machine vision technologies have been widely applied in the field of agricultural product inspection ( Gao et al, 2020 ). Near-infrared spectroscopy can more accurately detect the internal composition of a sample, such as protein and moisture ( Serrano et al, 2021 ).…”

Non-Destructive Internal Defect Detection of In-Shell Walnuts by X-ray Technology Based on Improved Faster R-CNN

“…After picking, internal defects, such as protein deterioration, flavor loss, shriveled seed kernel, and empty shell, and external mechanical damage in walnuts can occur during transportation, processing, and storage [2]. These seriously reduced the grade and commodity rate of walnuts and greatly weakened their market competitiveness [3]. At present, the detection methods used to identify the internal quality of walnuts are manual detection, physical means, or stoichiometry.…”

“…Van et al [13] and Tim et al [14] reported the internal-defects detection of apples and 'Conference' pears, achieving accuracy rates of 90% and 90.2%, respectively. Gao et al [15] and Zhang et al [16] also successfully applied it to detect whether hard-shelled walnuts had become hollow and also to detect the size of the walnut kernel. Such X-ray image technology has led to promising results in real-time, non-destructive testing of internal defects in intact walnuts with shells.…”