Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a n...

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Vydáno v:Journal of sensors Ročník 2021; číslo 1
Hlavní autoři: Zou, Juan, Jiang, Hanjing, Wang, Qingxiu, Chen, Ningxia, Wu, Ting, Yang, Ling
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 2021
John Wiley & Sons, Inc
Témata:
Accuracy
Air pollution
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation networks
Circuit design
Classification
Deep learning
Fault diagnosis
Fertilizers
Machine learning
Methods
Monitoring
Neural networks
Neurons
Pesticides
Sensors
Software
Soil contamination
Sparsity
Wireless networks
ISSN:1687-725X, 1687-7268
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Shrnutí:The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.
Bibliografie:ObjectType-Article-1
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ISSN:1687-725X
1687-7268
DOI:10.1155/2021/1015391