Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed

Rybacki, P.; Niemann, Janetta; Bahćevandžiev, Kiril; Durczak, K.

doi:10.3390/s23052486

Cited by 7 publications

(7 citation statements)

References 59 publications

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“…The model was created using deep learning to classify the aflatoxin contamination level in cocoa beans. Four different pre-trained CNN types were used in the process: SqueezeNet [44], GoogLeNet [45], ResNet50 [46], and AlexNet [47]. The CNN architecture for the classification is presented in the following Fig.…”

Section: ) Deep Learning Modelingmentioning

confidence: 99%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”. 300 images were acquired through a mini studio equipped with UV lamps. The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.

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Section: ) Deep Learning Modelingmentioning

confidence: 99%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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“…The dynamic development of artificial intelligence (AI), machine learning (ML), and computer image analysis (CIA) has facilitated the process of extracting quality features of agricultural products, fruits, and vegetables based on shape [ 17 , 18 , 19 ], colour [ 20 , 21 ], texture [ 22 ], and light spectrum [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

Rybacki,

Niemann,

Derouiche

et al. 2024

Sensors

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The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.

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“…For instance, Zhu ( Zhu et al., 2019 ) developed a self-designed Convolutional Neural Network (CNN) to classify seven varieties of cotton seeds, achieving an accuracy rate exceeding 80%—outperforming residual networks and other traditional models. Similarly, Rybacki ( Rybacki et al., 2023 ) constructed a CNN with a fixed architecture comprising five alternating layers of Conv2D, MaxPooling2D, and Dropout. This model successfully identified seeds from three winter rapeseed varieties, attaining the highest validation accuracy of 85.6%.…”

Section: Introductionmentioning

confidence: 99%

A maize seed variety identification method based on improving deep residual convolutional network

Li,

Xu,

Song

et al. 2024

Front. Plant Sci.

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Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network’s learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model’s parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.

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Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed

Cited by 7 publications

References 59 publications

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

A maize seed variety identification method based on improving deep residual convolutional network

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