Species classification of brassica napus based on flowers, leaves, and packets using deep neural networks

Alom, M. S.; Ali, Yuwin; Islam, Tarequl; Uddin, Abdul Hasib; Rahman, Wahidur

doi:10.1016/j.jafr.2023.100658

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…According [55], the RMSE is the total of the squared root that are present between the predictions made by the model and the observation data. The RMSE helps in transforming the squared error into the original units of predictions by taking the square root of the squared score [56], [57]. The calculation is shown in the following way.…”

Section: ) Displays the Training Dataset's Dimensionsmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

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Section: ) Displays the Training Dataset's Dimensionsmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Five CNN models were tested, and DenseNet201 classified both species with 100% accuracy for flowers and 97% for packets and leaves. These methods will be compared to metabolomics data in future studies (Alom et al, 2023) features and optimization with 94.6% classification accuracy (Dash et al, 2023). This study uses USG images and deep transfer learning to determine fetal sex.…”

Section: Literature Reviewmentioning

confidence: 99%

Model Performance Evaluation: VGG19 and Dense201 for Fresh Meat Detection

Hindarto

2024

SinkrOn

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To guarantee consumer safety and meet quality expectations, accurate detection of meat quality is a critical component of the food industry. The objective of this research endeavor is to assess and contrast the fresh meat detection capabilities of two distinct artificial neural network architectures, denoted as Dense201 and VGG19. Automated systems that can identify vital qualities in fresh meat, including color, texture, and cleanliness, have become feasible due to the development of image processing technology. For this reason, however, there are still few direct comparisons between various architectures of artificial neural networks, particularly VGG19 and Dense201. Comparing and contrasting the performance of both models in identifying the quality of meat from visual images, this study attempts to fill this void. Utilizing a vast dataset containing a variety of fresh meats exhibiting substantial visible variations constituted the research methodology. The assessment was conducted by examining the efficacy of both models in determining the quality of meat using established performance metrics, including accuracy, precision, recall, and F1-score. Regarding the detection of fresh meat, it is anticipated that the findings of this study will offer a comprehensive understanding of the benefits and drawbacks associated with every artificial neural network architecture. Contributing to a greater comprehension of the application of precise and efficient meat detection technology, this study also furnishes the food industry with a foundation for determining which model best meets the requirements of meat quality detection on a larger production scale.

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“…The advancement of deep learning, especially the advent of convolutional neural networks (CNNs), has supported diverse image-based classification and recognition due to its robust feature extraction and task transferability (Yasar, 2023). Alom et al (2023) successfully classified flowers, stems, and leaves of two oilseed rape varieties using transfer learning with five neural networks in visible light crop classification studies. Their method involving background removal and CLAHE preprocessing achieved 100% accuracy in flower classification and 97% in stem and leaf classification.…”

Section: Introductionmentioning

confidence: 99%

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model

Pan,

Yu,

Dong

et al. 2024

Front. Plant Sci.

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IntroductionIn agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues.MethodsG-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11’s fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency.ResultsRigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image.DiscussionThis validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.

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Species classification of brassica napus based on flowers, leaves, and packets using deep neural networks

Cited by 7 publications

References 8 publications

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Model Performance Evaluation: VGG19 and Dense201 for Fresh Meat Detection

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model

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