Research PaperPeanut maturity classification using hyperspectral imagery
Introduction
Cultivated peanut (Arachis hypogaea L.) is an important agronomic legume grown mainly in tropical and subtropical areas. Peanut agronomic production can be complex due to the crop's unique geocarpic reproduction, susceptibility to many fungal pathogens, and complex mechanical harvest procedure requiring separate inversion and threshing operations. Further, peanut has an indeterminate growth habit, thus having pods at various maturity levels throughout a growing season and at harvest time. It is critical for growers to accurately determine pod maturity to be able to maximize seed weight and quality, thus leading to optimal economic returns. However, the geocarpic fruit habit of peanut increases the difficulty in determining the percent of pods that are mature and ultimately to determining the optimum digging time. If digging occurs too early in development, immature pods can cause poor yield, grade, seed quality and flavour. If digging occurs too late, over-mature pods can detach from the vine during the digging or the threshing process.
Several methods have been developed for growers to predict and optimize harvest time. The most commonly accepted method is to remove the exocarp from the pericarp (hull) and categorize the inner mesocarp colour. This method was standardized by Williams and Drexler (1981) who created a Maturity Profile Board (MPB) that provided colour classification into five main colour categories (white, yellow, orange, brown, and black) and sub-categories within representing various shades within the main colour categories. Mesocarp colours of black and brown indicate mature seeds and conversely, orange, yellow, and white represent immature seeds. Based on the ratio of pods within various main and shade categories, growers can determine the optimal number of days until digging the crop (the first stage of harvest). This remains the primary method utilized by producers and researchers to date for evaluating seed maturity.
However, the MPB method has many flaws and disadvantages. First, it involves exocarp removal, often requiring “pod blasting” using a pressure washer. In this process, most white pods are blown apart and lost in the analysis due to their fragile pericarp structure and high water content. Further, this process can be extremely time consuming to reach the level of exocarp removal for accurate visual mesocarp assessment while not destroying too many of the pods in the process. Second, once the exocarp is removed, pods must be colour categorized using the human eye, a process that is extremely subjective (due to observer variability in colour categorization, lighting conditions, observer fatigue, etc.), thus introducing large error potential into the process. In addition, the process of visually categorizing pods can be very time consuming as well because it involves the individual placement of 100–150 pods within main and shade colour categories. For these reasons, the current process for determining peanut maturity is flawed and in need of a method that is objective and does not require exocarp removal.
A peanut pod maturity estimation approach was recently proposed (Bindlish, Abbott, & Balota, 2017) to replace the subjective colour categorization process with a simple Nearest Neighbour classifier that classifies the median RGB colour of mesocarp of each peanut into one of ten pre-set colour classes (i.e. ten maturity levels). In other words, it learns a simple mapping from RGB values of mesocarp images to peanut maturity. However, the time-consuming pod blasting step to remove the exocarp is still necessary in this method.
This challenge, to estimate peanut maturity on the exocarp level without pod blasting, lends itself to an evaluation of the peanut pericarp using more sophisticated imaging techniques. The current project explored using a hyperspectral imaging (HSI) process capable of detecting materials that differed in chemical composition. The colour change process in peanut involves the accumulation of tannins and other polyphenols within the mesocarp layer. It was reported that in hulls and seed coats, the tannin content increased significantly as peanut pod developed and showed a close relationship between tannin and maturity (Sanders, 1977), thus leading to a change in chemical composition within the pericarp that corresponds to seed development. If an HSI process could detect pericarp hyperspectral signatures indicative of each of the major colour classes, then peanut maturity could be determined non-destructively.
Section snippets
Theoretical framework of the HSI
The development of the HSI process involved the collection of a high dimensional hyperspectral image data cube. This cube consisted of a stack of hundreds of two-dimensional images collected at different wavelengths across the electromagnetic spectrum, as illustrated in Fig. 1. The spectral signature collected across all measured wavelengths associated with each pixel in a hyperspectral image is composed of the radiance values from each of the measured wavelengths and characterizes the chemical
Results and discussion
In this study, the maturity estimation and classification experiments were trained on peanuts collected in 2016 and tested on peanuts collected in 2017, and vice versa.
Conclusion
This study successfully developed and tested a peanut maturity classification system that involved an objective, non-destructive technique utilising HSI. In this method, we were able to evaluate individual pod maturity with intact exocarp layers, thus eliminating the need for peanut blasting. We found that peanuts at different maturity levels have different spectral signatures when imaged with a hyperspectral sensor. Therefore, we were able to leverage spectrum difference to estimate pod
Declaration of competing interest
None declared.
Acknowledgement
This work was supported by National Peanut Board, USA; Florida Peanut Producers Association, and National Science Foundation, Division of Information and Intelligent Systems, USA [grant number #1723891 “CAREER: Supervised Learning for Incomplete and Uncertain Data”].
Nomenclature table
- ci
- Confidence value of ith image region, a scalar in
- The dimensionality of pixel spectrum, an integer in
- -th endmember spectrum, a column vector in
- A matrix of endmembers, , a matrix in
- Noise term for-th pixel spectrum, a vector in
- The number of endmembers, an integer in
- The number of pixels, an integer in
- The proportion of in-th pixel spectrum, a scalar in of [0,1]
- A collection of , a matrix in
- Classification threshold, a scalar in of
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These authors contributed equally to this work.