Abstract
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.
Highlights
Agricultural product traceability systems have been gradually applied to the actual production process, but manually entered traceability information is difficult to gain the trust of consumers and regulators, and a lack of trust in traceability information has become one of the main factors hindering the uptake of traceability systems
After the agricultural inputs were applied to the soil, the data collected by the sensors were impacted by the physical and chemical properties of the inputs
This paper tested an input prediction model based on SSDAHELM-SOFTMAX to predict inputs in real time
Summary
Agricultural product traceability systems have been gradually applied to the actual production process, but manually entered traceability information is difficult to gain the trust of consumers and regulators, and a lack of trust in traceability information has become one of the main factors hindering the uptake of traceability systems. To prevent agricultural input pollution such as fertilizers and pesticides used in the production process, traceability systems are currently mainly used to record agricultural residue testing reports. Research on real-time online prediction of Journal of Sensors agricultural inputs based on deep learning is highly significant, which can improve the accuracy of input prediction and ensure the timeliness and accuracy of the traceability information. Yi et al used a photoluminescence sensor for ultrasensitive detection of pesticides [12] These methods were residual detection after implementation, and input information needed to be recorded manually, which could not guarantee the real-time and accuracy of the traceability system. An agricultural input classification prediction model based on SSDA-HELM-SOFTMAX was established, which laid the foundation for accurate classification prediction of agricultural inputs
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