Discrimination of Different Types Damage of Tomato Seedling by Electronic Nose

Abstract

Abstract:The profiles of volatile compounds emitted by plants varies in response to different damage.The potential of electronic nose technology to monitor such changes,with the aim of diagnosing plant health was investingted.An electronic nose(E-nose)was used to analyse tomato seedlings that were subjected to differnent types of damage(infection by Early blight disease, infection by Gray mold disease, mechanically damage, and undamaged). Principal component analysis(PCA),linear discrimination analysis(LDA),back-propagation neural network(BPNN),and support vector machine (SVM) network were used to evaluate the E-nose data.The results indicated that the E-nose can successfully discriminate between tomato seedling with different types of damage. The results of PCA and LDA showed that clusters of data were divided into 3 groups (ZP, HP, and CP/MP). Samples from groups CP and MP overlapped partially. Back-propagation neural network (BPNN) and support vector machine (SVM) network were used to evaluate the E-nose data. Good discrimination results were obtained using SVM and BPNN. The results demonstrate that it is plausible to use E-nose technology as a method for monitoring damage in tomato seedling.

Quality of tomatoes under different transportation conditions by principal component analysis

“…Azodanlou et al (2003) also used PCA to investigate tomato quality based on its sensory characteristics, and sweetness and aroma were the attributes of highest relevance for the analysis of quality. Indirect parameters obtained by electronic sensors (Cheng et al, 2017) have also been tested for automated evaluation of the quality of industrial tomatoes.…”

Plant pest surveillance: from satellites to molecules

“…E-noses are appealing as in-field systems for the rapid detection of plant VOCs and have been used for the diagnosis of diseases including fire blight (Erwinia amylovora) and blossom blight (Pseudomonas syringae pv. syringae) in asymptomatic apple trees [61][62][63] and early blight disease/gray mold disease of tomato seedlings [64]. E-nose devices have several limitations restricting their adoption as a diagnostic tool including signal drift, susceptibility to humidity, moderate sensitivity, complex data processing, and high purchase cost.…”

Plant Pest Detection Using an Artificial Nose System: A Review

“…Infected rice plants attacked by the striped rice stem borer ( Chilo suppressalis ) and the brown planthopper ( Nilaparvata lugens ) can be easily discriminated from healthy plants. Furthermore, the extent of damage to the rice plant as well as the amount of pests can also be successfully estimated based on the sensor response of an E-nose [ 88 ]. Volatiles released from insects such as brown planthoppers and stink bugs might be another avenue for revealing different aspects of insect-infested plants.…”

“…The combination of an E-nose system with mass spectrometry or gas chromatography could address these challenges and extend the application of these technologies. Inspired by comprehensive sensory evaluation of biological senses, an electronic tongue that mimics the human sense of taste could be combined with an E-nose to establish a more robust and more widely applicable platform, especially for applications requiring liquid detection or in areas with high humidity [ 87 , 88 ].…”