Floriculture classification using simple neural network and deep learning

Dharwadkar, S. N.; Bhat, Ganesh; Reddy, N. V. Subba; Aithal, Prakash K.

doi:10.1109/rteict.2017.8256671

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…The success rates for dead/alive plant detection for the LiDAR and light curtain sensors were 93.75% and 94.16%, respectively. Additionally, a few other studies have reported the application of machine vision approaches using different machine learning and deep learning methodologies for detecting and classifying different flower nurseries [ 71 , 80 , 81 , 82 , 83 , 84 ].…”

Section: Sensing and Automation Technologies For Ornamental Cropsmentioning

confidence: 99%

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Mahmud

Zahid

Das

2023

Sensors

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The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.

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Section: Sensing and Automation Technologies For Ornamental Cropsmentioning

confidence: 99%

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Mahmud

Zahid

Das

2023

Sensors

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“…Aakif & Khan in 2015 used an ANN with a unique shape feature, morphological features, and Fourier descriptors and achieved the highest accuracy of 96% [9]. The Shape Defining Feature (SDF) of calculating the shape feature involved calculating gradients on the edge of the leaf [10].…”

Section: Related Workmentioning

confidence: 99%

Plant Classification based on Leaves using Artificial Neural Network

Desai

Savani

Patel

2021

IJIE

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Manual methods to examine leaf for plant classification can be tedious, therefore, automation is desired. Existing methods try distinctive approaches to accomplish this task. Nowadays, Convolution Neural Networks (CNN) are widely used for such application which achieves higher accuracy. However, CNN's are computationally expensive and require extensive dataset for training. Other existing methods are far less resource expensive but they also have their shortcomings for example, some features cannot be processed accurately with automation, some necessary differentiators are left out. To overcome this, we have proposed a simple Artificial Neural Network (ANN) for automatic classification of plants based on their leaf features. Experimental results show that the proposed algorithm able to achieve an accuracy of 96% by incorporating only a single hidden layer of ANN. Hence, our approach is computationally efficient compared to existing CNN based methods.

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“…The original classification of fresh-cut flowers is done manually [1], but owing to human subjectivity, visual fatigue, experience differences, and classification efficiency, it is difficult to meet the requirements of standardized production of freshcut flowers and cannot guarantee the efficiency, consistency, and stability of grade classification and quality evaluation of fresh-cut flowers. To effectively reduce the labor intensity of workers, manual intervention, and the loss of post-harvest processing of fresh-cut flowers, achieve high efficiency and quality, and meet the demands of automatic processing and classification line of fresh-cut flowers, it is necessary to use automatic processing and classification systems [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…) is the result of the horizontal flipping operation on the original image, image(2) is the result of the vertical flipping operation on the original image, image (3) is the result of the horizontal and vertical flipping operations on the original image, image (4) is the result of a 90 °rotation operation on the original image, image (5) is the result of adding random salt and pepper noise to the original image, image (6) is the result of randomly adding Gaussian noise to the original image, image(7) is the result of the blurring operation on the original image, and image (8) is the result of changing the brightness of the original image.Because of the different sizes of the minimum rectangular boxes used for capture and cropping, the sizes of the images after cropping were different. To facilitate the subsequent model training and testing, the samples were adjusted to a uniform size, and a 224×224 3C flower dataset with 3906 samples was obtained after data augmentation.…”

mentioning

confidence: 99%

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Fei

Zhu

et al. 2023

IEEE Access

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In the process of classifying fresh-cut flowers, the classification accuracy of the algorithm plays a vital role in the control of quality stability, uniformity, and price of fresh-cut flowers, while the classification speed of an algorithm determines the possibility of industrial application. Currently, research on fresh-cut flower classification focuses on the breakthrough of classification accuracy, ignoring the real-time processing speed of the terminal, which seriously affects the use of fresh-cut flower online classification technology. In this study, RGB images and depth information data for 434 rose flowers were collected using a binocular stereo depth camera. Combined with the actual production line classification environment, a set of data argumentation solutions was developed under the condition of limited samples. The architecture was established and optimized based on the ShuffleNet V2 network backbone unit, transfer learning was performed, and an appropriate attention mechanism was invoked to classify flowers of five specifications. The experimental results showed that the proposed network structure had a competitive advantage in terms of parameter quantity, classification speed, and accuracy compared with traditional networks without an attention mechanism and other lightweight networks. The classification accuracy on the 3-channel (RGB channel) flower dataset and the 4-channel (RGB and depth channel) flower datasets were 98.891% and 99.915%, respectively, and the overall prediction classification speed can reach 0.020 seconds per flower. Compared to the fresh-cut flower classification machines currently on the market, the speed of the proposed method has a great advantage. These advantages are of great significance for the design and development of fresh-cut flower classification and grading systems, and the proposed method is instructive for the identification and application of multichannel data in the future.

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Floriculture classification using simple neural network and deep learning

Cited by 12 publications

References 7 publications

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

Plant Classification based on Leaves using Artificial Neural Network

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

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