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In this review, the aim was to (1) briefly describe the seed processing steps, (2) review the past and current efforts to efficiently analyze big datasets derived during seed processing through ML algorithms, and (3) provide a future direction in this area to facilitate seed processing steps using novel mathematical methods. Different types of ML algorithms were exploited in different steps of seed certification. Each tested algorithm has its advantages and disadvantages. In this section, some important algorithms in the seed certification process are explained in detail.
Artificial neural networks (ANNs)
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ANNs algorithms are one of the most broadly used algorithms in seed recognition research[21]. ANN-based modeling structures are stimulated by the human brain's neurological processing ability. It could be considered as an encouraging approach for managing the nonlinearities and complexities of complex processes like seed recognition that is replete with incalculable, noisy, fractional, and missing data. ANN-based models could make models of compound dynamic structures without an understanding of the comprehensive basic physical mechanisms in multivariate traits. There are various ANN models, though the basic principle is similar[22].
An ANNs model contains several neurons as signal-processing components that are connected by synapses as unilateral communication channels. An ANN receives input signals, processes the received signals, and finally produces an output signal. Every ANNs is linked to at least one other neuron based on its significant degree of a particular connection to the network that is called the weight coefficient[23]. The generated signals from other neurons are considered as input data of [X1, X2, ... , Xm]. The input data are multiplied by their associated synaptic weights (Wkj) and then moved to the artificial neuron. Furthermore, a bias input (bk) is considered an additional input signal to the artificial neuron. The Strength of the incoming signals is calculated by aggregating weighted input data and the bias input (Σ(weight*input ) + bias)[24]. An activation function (λk) is imposed to model that decreases the domain of incoming signals into a limited value, and finally, the non-linear output (Yk = f Σ (weight * input) + bias ) is generated. Activation functions lead to the no-linear transformation of input that makes the ANN capable of learning and performing complex tasks (Fig. 2). The most common activation functions in ANN systems are binary step, identity, softmax, sigmoid/logistic, tangent, hyperbolic tangent, and Gaussian[25].
ANN models are classified on the basis of their learning mode into supervised/unsupervised or on their structures into feed-forward or feedback recall methods[26]. Supervised ANNs uses a set of data patterns in the learning procedure that has separate input and output. However, unsupervised ANN algorithms employ a set of data patterns in the training process with only input values. Feedforward ANN models exploit unilateral information processing approaches that transmit data only from inputs to outputs. In contrast, feedback ANN models employ the bilateral stream that results in achieving insights from the prior layers letting feedback to the next layers in any neuron[26].
Synapses transfer numerical weights to diminish the error between real and simulated data in several training algorithms. However, optimizing ANN models has some complications including data compilation, data processing, topology selection, training and testing the selected model, and simulation and validation of the established ANN models.
There are several types of ANNs that every have been developed into particular problems and applications. Some networks are appropriate for solving conceptual complications, whereas others are proper for data modeling and function estimation. The most popular ANNs in seed classification and recognition are Kohonen networks, multilayer perceptron (MLP), networks, deep neural networks (DNNs), and convolutional neural networks (CNNs) (Fig. 3)[22].
Kohonen network
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Kohonen network algorithm, known as self-organizing maps (SOM), is a type of unsupervised learning algorithm, consisting of two-layer networks where the input and output layers are completely connected where similar patterns are promoted in the vicinity to one another that results in a 2D map of the output neurons (Fig. 3)[27]. Neurons that are closest to input win, and only weights of the winning neurons and their neighbors are updated. Kohonen networks map high-dimensional data into a smaller space that leads to data compression. Kohonen networks are employed for clustering (data grouping), pattern recognition, image segmentation, fuzzy partitioning, and classification[28]. Chickpea seed varieties were identified through unsupervised ANNs, a self-organizing map (SOM) that showed a better performance with 79% accuracy compared to supervised ANN with 73% accuracy. SOM can learn new things and changes with variable conditions and inputs. However, unsupervised learning algorithms do not result in expected outputs[29]. Besides, classification and identification of plants were made in leaf blade samples by the ANNs based on backpropagation algorithm (BP), KNN algorithm, Kohonen network based on SOM algorithm, and support vector machine. The training and identification time of the Kohonen network is moderately short, but the error rate of the Kohonen network is also very high due to the Kohonen network being applied without supervision (Table 1). Comparisons between four algorithms indicated that it could be effective for clustering, but it is not proper as a classifier and cannot deliver sufficient information to separate from other items[30].
Table 1. Comparisons among ML algorithms.
ML algorithms SOM MLP DA SVMs RF NB CNNs Accuracy + +++ ++ ++++ +++++ +++ ++++++ Flexibility + ++ + +++ ++++ +++ ++++++ Advantages Fast Non-linear classification Quick, inexpensive Analyzing complex networks, diminishing the generalization error, using large number of hidden units Tolerant of
highly correlated predictorsSimplicity,
fast training, decreases the number of parametersAnalysis of massive
amounts of
unsupervised data, better classification
and predictionDisadvantages Unsupervised Time-consuming, few hidden neurons Unsupervised Algorithmic complexity, development of ideal classifiers for multi-class problems and unbalanced data sets Evaluation
of pairwise interactions is difficult, future predictions require the original dataOversensitive
to redundant
or irrelevant attributes, classification
biasIt needs further developments
for big data
analysisMultilayer perceptron (MLP)
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Multilayer perceptron (MLP) is a class of feed-forward artificial neural networks developed by[31]. MLP is a non-linear computational process that is highly efficient for the classification and regression of complex features. Furthermore, MLP was created to address non-linear classification problems that further layers of neurons employed between the input layer and the output neuron, and these neurons are called hidden layers (Fig. 3). Thus, these hidden layers process the information achieved from the input layers and process them to the output layer that develop perceptrons to resolve non-linear classification problems[32]. MLP is frequently employed for modeling and forecasting complex attributes, such as yield[33], classification seed varieties[7], weed discrimination[34], unknown seed identification[35]. This algorithm discovers the relationship between the input and output variables through some interconnected processing neurons that identify a solution for a particular problem[36]. However, MLP is time-consuming method that may result in inaccurate modeling. Besides, MLP regularly employs few hidden neurons that makes it inappropriate for modeling and predicting than other algorithms with more hidden neurons[33] (Table 1).
Discriminant analysis (DA)
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Discriminant analysis (DA) is a flexible classifier that was exploited to classify observations into two or more groups or classes, and it examines methods and degrees of the contribution of variables to group partitioning (Figs 4 & 5). It was employed for image processing by several scholars in textural analysis in wheat (Fig. 5)[37] and color analysis in castor[38]. In wheat, a stepwise discrimination system was exploited for selection and ranking of the most important textural features by LDA (linear discriminate analysis) classifier with 98.15% accuracy in top selected traits in nine cultivars[37]. Furthermore, color analysis in castor through partial least squares-discriminant analysis (PLS-DA) and LDA demonstrated that the PLS-DA model with 98.8% accuracy was more efficient than LDA. It showed that this method was straightforward, quick, beneficial, and inexpensive (Table 1)[38].
Chen et al. analyzed 28 color features of five corn varieties by step-wise discriminant with 90% accuracy[39]. In addition, the color and shape attributes of Italian landraces of the bean were investigated using LDA that showed 82.4%−100% accuracy[40]. However, another research used the shape plus texture features to recognize two weed species, rumex, and wild oat, in Lucerne and Vetch. They employed ANNs and stepwise discriminant analysis, while the ANNs presented better precision (92%−99 %) than the DA[41].
Support vector machines (SVMs)
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Support vector machines (SVMs) introduced by Vapnik in 2000 can be considered one of the most prevailing and simple machine learning algorithms[42]. SVMs can be categorized based on the output variable to the Support Vector Classification (SVC) that classifies data, and the Support Vector Regression (SVR), which determines regression[6]. The data was separated into training and validation sets. The majority of the dataset was assigned to the training set, whereas the rest was partitioned into the validation set based on different approaches such as cross-validation (Fig. 4)[43].
SVMs are usually applicable to a two-class problem that creates a boundary between two groups in linear, non-linear, and trade-off penalty parameters to handle complexity (Fig. 4)[44]. In non-linear relationships, SVM can discover patterns and performances. SVMs have several benefits over MLP depending on the dataset, which can analyze complex networks and employ numerous learning problem formulations to solve a quadratic optimization problem[45]. Besides, SVMs showed a greater advantage over ANNs due to diminishing the generalization error by exploiting the structural risk minimization principle rather than the practical one used in ANNs[21]. Even though there are several benefits of the SVM, there are some noticeable flaws including, algorithmic complexity leading to longer training time of the classifier in large data sets, development of ideal classifiers for multi-class problems, and unbalanced data sets (Table 1)[46]. SVM was employed in several studies for discrimination and classification (Table 2). For soybean seed discrimination, several morphological and color attributes from several seed classes were analyzed by SVMs. Results showed that color traits had better discrimination ability than morphological traits with 77% and 59% accuracy, respectively[47]. When the purity of waxy corn seeds was identified by image analysis of morphological and texture features through SVM that data showed 98.2% accuracy[48]. Furthermore, an SVMs classifier was employed to detect seed defects in a large volume of corn seeds using color and texture features analysis. The results showed that the best accuracy (81.8%) was obtained through the combination of both color and texture analysis than color and texture individually[49]. The same method was exploited to identify corn varieties with the best accuracy (94.4%)[50].
Table 2. Examples of applied machine learning models in modern seed recognition and classification studies.
Plant species Type of machine learning Classifier Accuracy Features Purpose Ref. Corn Digital image MLP 98.83% Texture - spectrum hybrid Seed varietal purity [10] Wheat Digital image ANNs 85.72% Morphology Seed varietal purity [7] Wheat Digital image ICA-ANN hybrid 96.25% Color, morphology,
and textureSeed varietal purity [72] Wheat Digital bulk image LDA 98.15% Texture Seed varietal purity [37] Wheat Digital bulk image ANNs 97.62% Texture Seed varietal purity [73] Forage grass (Urochloabrizantha) FT-NIR spectroscopy & X-ray imaging RF 85% Spectrum-composition hybrid Seed germination & vigor [74] Corn FT-NIR spectroscopy PLS-DA 100% Chemical composition Seed germination & vigor [75] Pepper FT-NIR & Raman spectroscopy PLS-DA 99% Chemical composition Seed germination & vigor [76] 57 weed species Digital image NB & ANNs 99.5% Color, morphology,
and textureWeed identification [59] Wheat Video processing ANN - PSO hybrid 97.77% Shape, texture & color Physical purity & weed identification [77] Rice Digital image MLP 99.46% Morphology, texture & color Seed varieties classification [9] Rice Digital image ANNs − Morphology Seed grading [78] Rice Digital image DFA 96% Morphology Physical purity [79] Soybean Aerial imagery CNNs 65% Object detection Weed identification [80] Soybean Digital image CNNs 97% Color, texture and shape Seed deficiency [66] Soybean Digital image CNNs 86.2% Color, texture and shape Seed counting [70] Corn Digital image MLP 94.5% Color Physical purity [81] Soybean Flatbed scanner SVMs & RF 78% Color Seed grading [47] Bean Digital image RF 95.5% Color, texture and shape Seed varieties classification [16] Corn Hyperspectral image SVMs 98.2% Spectrum-texture – morphology hybrid Seed varieties classification [48] Corn Digital image SVMs 95.6% Color and texture Seed varieties classification [49] Corn Digital image GA–SVM hybrid 94.4% Color, texture and shape Seed varieties identification [50] Corn Digital image CNNs 95% Color, texture and shape Haploid and dioploid discrimination [82] Corn Digital image CNNs 95% Color, texture and shape Seed varieties identification [83] Barley Digital image DA & K-NN 99% Color, morphology & texture Seed varieties classification [8] Barley Digital image CNNs 93% Color, morphology & texture Seed varieties classification [84] MLP: Multilayer Perceptron, ICA: Imperialist Competitive Algorithm, ANNs: Artificial Neural Networks, LDA: linear discriminate analysis, FT-NIR: Fourier transform near-infrared, PLS-DA: partial least squares discriminant analysis, NB: naïve Bayes, PSO: partial swarm optimization, DFA: stepwise discriminant function analysis, CNNs: Convolutional neural networks, SVM: support vector machine, GA: genetic algorithm, DA: discriminant analysis, K-NN: K-nearest neighbors, SVMs: support vector machines. Random forest (RF)
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Random forest (RF) was developed by Breiman[51] that is known as an effective method for seed classification[16], object recognition[52], plant phenomics, and genomics[53]. The RF is based on multiple decision trees, which are built at the same time (Fig. 4). They are constructed by bootstrapping data samples to learn, similar to bagging (bootstrap aggregation).
During training for each tree, the bootstrapped data samples are used as observations, and another randomly chosen beech of bootstrapped data samples is used as an out-of-bag observation. This is done repeatedly until every sample has been left out of one bag. Out-of-bags are used as an input for a newly built random forest with the same number of trees[54]. The RF algorithm can be extended to multiclass, sequential regression, and binary classification problems. The RF algorithm has been extended to the tree pruning problem, called 'non-Breiman random forests'. In this regard, it is possible to generate resampled trees and use them as a candidate for each node in the constructed decision tree. The selection of the best tree is made based of mean-square error[16]. Despite these advantages, RF generates a large number of candidate predictors that makes the evaluation of pairwise interactions difficult. Also, future predictions require the original data due to no possibility of replicating predictions without an actual forest[55].
RF along with SVMs were exploited to discriminate soybean varieties based on color and morphological features. Data showed RF classifiers discriminated color features better than morphological features with a better accuracy 78% better than SVMs (77%)[47]. However, three algorithms including, RF, SVMs, and K-Nearest Neighbors (KNN) were employed to classify dry beans through a computer vision system for seed certification. The results showed the better accuracy of the KNNs classifier (95%), while the accuracy of RF and SVMs was 93.1% and 93.5% respectively. The RF model accuracy exceeded 95.5% when using principal component transform was compared with the original variables[16]. Another research classified rice through image features of rice seed including, color, shape and texture. RF classifiers was employed along with SVMs, while RF classifiers through simple features showed the greatest classification with accuracy of 90.54% than SVMs[56].
Naive Bayes (NB)
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Naive Bayes (NB) classifiers are simple probabilistic classifiers based on the Bayes hypothesis that implies the independence of pair traits[57]. In comparison with other classifiers like Neural Networks and Support Vector Machines, NB calls for quite little data for training, thus it does not include several parameters. It trains data fast and simply implemented. However, NB is oversensitive to excessive or unrelated attributes. When some attributes are extremely correlated, they take great weight in the final decision, resulting in a decrease in accuracy of prediction of correlated features and classification bias[58]. It was employed in several studies to identify and classify different seeds and plants (Table 2). NB is usually employed to recognize weed seeds based on morphological, color, and textural characteristics from images[59]. This algorithm decreases the number of parameters to nearly ideal sets in every feature[60]. The NB classifier outperformed ANN algorithms in weed seeds identification[34]. Moreover, it was exploited in the seeds classification of Kama, Rosa, and Canadian wheat varieties according to their morphological features as the second-highest accuracy classifier (94.3%), though ANN algorithms showed the highest performance (95.2%) than NB[61].
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All the data presented in this study are available from the corresponding author.
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About this article
Cite this article
Ghaffari A. 2024. Precision seed certification through machine learning. Technology in Agronomy 4: e019 doi: 10.48130/tia-0024-0013
Precision seed certification through machine learning
- Received: 02 December 2023
- Accepted: 07 May 2024
- Published online: 23 July 2024
Abstract: Original and pure seeds are the most important factors for sustainable agricultural production, development, and food security. Conventionally, seed protection and certification programs are carried out on several classes from breeders to certify seed based on a physical, biochemical, and genetic evaluation to approve seed as a cultivar. In seed industries, quality assurance programs depend on different methods for certifying seed quality characteristics such as seed viability and varietal purity. Those methods are mostly conducted in a less cost-effective and timely manner. Combining machine learning (ML) algorithms and optical sensors can provide reliable, accurate, non-destructive, and quick pipelines for seed quality assessments. ML employs various classifiers to authenticate and recognize varieties through K-means, Support Vector Machines (SVM), Discriminant Analysis (DA), Naive Bayes (NB), Random Forest (RF) and Artificial Neural Networks (ANNs). In recent years, progress in ANN algorithms as deep learning simplifies big data analytics procedures by categorizing learning and extracting distinct levels of multiplex data. Deep learning opened a new door for developing a smartphone as a fast and robust substitute for the online seed variety discrimination stage through developing a Convolutional neural networks (CNNs) model-based mobile app. This review presents machine learning and seed quality assessment areas to recognize and classify seeds through long-standing and novel ML algorithms.