Seed Segregation using Deep Learning

Hiremath, Swathi K; Suresh, S. P.; Kale, Sanjana; Ranjana, R.; Suma, K; Nethra, N.

doi:10.1109/ghci47972.2019.9071810

Cited by 12 publications

(3 citation statements)

References 1 publication

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“…Hiremath et al. [9] proposed to identify defective seeds based on seed visual characteristics and a novel CNN. Uzal et al.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, significant progress has been made in crop identification and pest detection [7,8]. Hiremath et al [9] proposed to identify defective seeds based on seed visual characteristics and a novel CNN. Uzal et al [10] constructed estimates of the number of seeds in the soybean pods system based on CNN and SVM, and Veeramani et al [11] distinguished maize seeds through features extracted by CNN.…”

Section: Introductionmentioning

confidence: 99%

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Real‐time seed sorting system via 2D information entropy‐based CNN pruning and TensorRt acceleration

Liao

et al. 2023

IET Image Processing

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Seed sorting based on deep neural networks is one of the important applications of seed variety identification and quality purification. However, DNNs is difficult to deploy on embedded devices since the consumption of computational and storage resource. To address these problems, this paper proposes a pipeline‐style neural network framework for real‐time seed sorting. First, we propose a novel algorithm, 2D information entropy, pruning redundant filters to realize structured pruning. Then, the pruning rate of each convolution layer is determined by visualizing the results of 2D entropy. Meanwhile, the pruned network is fine‐tuned to recover the performance. Finally, TensorRT is utilized to optimize and accelerate the pruned model for deployment in Jeston Nano. Experiments on two large‐scale seed‐sorting datasets demonstrate the significant improvement of the proposed method over existing model compression methods. Experimental results on Jeston Nano show that the pruned model 2EFP‐E achieves a single image inference speed of 107 FPS, with the best accuracy of 95.94% on the red kidney bean dataset.

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“…Hiremath et al. [9] proposed to identify defective seeds based on seed visual characteristics and a novel CNN. Uzal et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real‐time seed sorting system via 2D information entropy‐based CNN pruning and TensorRt acceleration

Liao

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Because of the test, the achievement pace of haploid maize seed characterization was determined as 94.25% and the achievement pace of diploid maize seed order was 77.91%. The authors of [29] discussed A prevalent harvest yield is an indispensable piece of the farming business. The chief part for a decent yield is great quality seeds.…”

mentioning

confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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Classification of grains according to their type becomes very much necessary because there are multiple grains available in the market today. Classification of Grains according their types manually is not efficient. Classification becomes a really wearisome task when it comes to doing it hand-operated instead of automating the process. Manually classifying would be a grueling task. There is a need for mentally keen and quick system which can get better of this difficulty by automating these processes. It should be able to recognize and label the every single grain according to their respective type automatically. Therefore we conducted a literature survey on how various seeds were classified and various analysis which were used to obtain the various desired result. Here we observed that various researchers used k-means, CNN and various other algorithms.

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A Comparative Study on Machine Learning Based Classifier Model for Wheat Seed Classification

Sujatha¹,

Sankareswari

2022

Mining Intelligence and Knowledge Exploration

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Seed Segregation using Deep Learning

Cited by 12 publications

References 1 publication

Real‐time seed sorting system via 2D information entropy‐based CNN pruning and TensorRt acceleration

Real‐time seed sorting system via 2D information entropy‐based CNN pruning and TensorRt acceleration

Literature Survey on Grain Type Prediction Using Machine Learning

A Comparative Study on Machine Learning Based Classifier Model for Wheat Seed Classification

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