Study On The Feasibility For Detection Of Tomato Seeds Germination And Disease-infestation Of Tomato Seedling By The Electronic-nose

Plants alter their profiles of emitted volatiles in response to damage or herbivore attack. This study investigated the potential of the electronic nose technology to monitor such changes, with the aim to diagnose plant health. In this study, electronic nose (E-nose) was used to analyse different treatment of tomato seeds and tomato plant samples. Several statistical methods such as feature extraction and pattern recognition were used for analyzing the experimental data. We have studied the relations between E-nose response signals and disease-induced volatiles, and developed the pattern recognition models. The results demonstrated that it is plausible to use E-nose technology as a method for monitoring damage in tomato cultivation practices. The main conclusions were as follows:(1) Research on detecting tomato seeds with different germinationAn investigation was made to evaluate the capacity of an electronic nose to classify the tomato seeds with different germination. The data were processed by Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA). The result shows that the electronic nose can distinguish the tomato seeds with germination percentage of 90%,80%,70%-50% and un-germination seeds. However, samples with germination percentage of 50%,60% and 70% were overlapped. The e-nose can distinguish the different time tomato seeds which blended each at different proportion by using BPNN and SVM. A better predicted rate was obtained by SVM than BPNN.(2) Research on detecting mechanical-damageThe value of E-nose response signals differed with different levels mechanical (Opricks,30pricks,60pricks and 90pricks) damaged tomato plants, indicating that the emission of volatiles by tomato plants changes in response to different degrees of damage. Stepwise discriminant analysis (SDA) and back-propagation neural network (BPNN) were applied to evaluate the data. The average correction ratios of testing set of SDA and BPNN were 84.4% and 90%. The results obtained indicate that it is possible to classify different degrees of damaged rice plants using e-nose signals.(3) Research on detecting Early blight disease and Gray mold disease infestation An E-nose equipped with a headspace sampling unit was used to discriminate tomato plants infested Early blight disease at different times. PCA resulted in an even distribution of the 4 different treatments with different disease densities. LDA was able to distinguish between all treatments. The discrimination rates were over 100% for training data sets and 50% for the testing sets, as determined using SDA in 24h later. The discrimination rates were over 97.5% for training data sets and 66.7% for the testing sets, as determined using SDA in 48h later. The discrimination rates were over 95% for training data sets and 87.5% for the testing sets, as determined using BPNN in 24h later. The discrimination rates were over 92.5% for training data sets and 79.2% for the testing sets, as determined using BPNN in 48h later. The results indicated that it is possible to predict the different diease densities in tomato plant using the E-nose.The discrimination rates were over 97.5% for training data sets and 70.8% for the testing sets, as determined using SDA in 24h later. The discrimination rates were over 82.5% for training data sets and 62.5% for the testing sets, as determined using SDA in 48h later. The discrimination rates were over 92.5% for training data sets and 87.5% for the testing sets, as determined using BPNN in 24h later. The discrimination rates were over 82.5% for training data sets and 66.7% for the testing sets, as determined using BPNN in 48h later. The results indicated that it is possible to predict the different disease densities in tomato plant using the E-nose.(4) Different characteristic value using for BPNN and GABPFour kinds of characteristic value (Mean,Kmax,IV and Max) were used to analyse the different infestation treatments of tomato paint. Back-propagation neural network (BPNN) and genetic algorithms back propagation network (GABP) were developed for pattern recognition models. When the models were used to predict infestation degrees using different feature selection methods. The highest coefficient of determination between predicted and real numbers of the disease using the IV method. Compare BP results with GABP results, the coefficient of determinations of the GABP were higher than the BP. The results demonstrated that after optimized the BP neural network with genetic algorithm better results were obtained.(5) Research on detecting different types of damageFrom the chart, the E-nose sensor response changed for tomato plants by 4 types of treatments(ZP, HP, CP and MP). The results of PC A 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. Similar results could be obtained when using CA. The results of the CA showed obvious differentiation between the tomato plant samples with different types of damage. 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 plant.