Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

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Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry

This paper introduces an optimized deep convolutional neural network (DCNN) using special banded 1D kernels at the convolutional and the pooling layers adapted for electronic nose (E-nose) data. It is used to classify multiple types of Chinese herbal medicine. The optimized DCNN network is composed of 5 special convolutional layers with 1D convolutional kernels, 2 special pooling layers with 1D size, 1 fully connected layer and 1 Softmax layer. Results show that the optimized DCNN achieves the best accuracy of 87.56%, outperforming the 81.67% from the second-best classifier DCNN. The optimized DCNN extracts features from E-nose data faster and better than common DCNN. This paper also proposes an insight of applying DCNN to small-scale and E-nose data.

An optimized deep convolutional neural network for dendrobium classification based on electronic nose

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible Instead of using posterior probability of a classifier, the proposed method uses the “agreement” of an ensemble to decide whether to accept or reject the candidate label The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives

Classifiers with a reject option for early time-series classification

Zero‐shot learning, applied with vision‐language pretrained (VLP) models, is expected to be an alternative to existing deep learning models for defect detection, under insufficient dataset. However, VLP models, including contrastive language‐image pretraining (CLIP), showed fluctuated performance on prompts (inputs), resulting in research on prompt engineering—optimization of prompts for improving performance. Therefore, this study aims to identify the features of a prompt that can yield the best performance in classifying and detecting building defects using the zero‐shot and few‐shot capabilities of CLIP. The results reveal the following: (1) domain‐specific definitions are better than general definitions and images; (2) a complete sentence is better than a set of core terms; and (3) multimodal information is better than single‐modal information. The resulting detection performance using the proposed prompting method outperformed that of existing supervised models.

Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

https://www.mdpi.com/1424-8220/12/3/2818/pdf

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

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Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application