Fundamentals and application of voltammetric electronic tongues in quantitative analysis

Altres ajuts: acords transformatius de la UABElectronic tongues (ETs) are bioinspired analytical tools based on the synergies between (bio)sensors and chemometrics. Through the application of chemometrics, it is possible to infer underlying relationships between the measured analytical signals and the chemical properties of the samples, both for descriptive and predictive purposes, otherwise impossible to decipher. Research in voltammetric ETs during the last two decades has demonstrated the benefits derived from the use of sensor arrays with complementary response, together with advanced data treatment methods to enhance their overall performance. In this direction, the different approaches followed when developing voltammetric ETs and some relevant applications in quantitative analysis are reviewed herein

HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION

Hyperspectral band selection and band combination has become a powerful tool and have gained enormous interest among researchers. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. An integrated principal component analysis (PCA) and Fisher linear discriminant (FLD) method has been developed for feature band selection, and other pattern recognition technologies have been applied and compared with the developed method. The results on different types of defects from cucumber and apple samples show that the integrated PCA-FLD method outperforms PCA, FLD and canonical discriminant methods when they are used separately for classification. The integrated method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications. Dimensionality reduction not only serves as the first step of data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirement for online automatic inspection of fruit. In this study, the hyperspectral research shows that the near infrared spectrum at 753nm is best for detecting apple defect. When applied for online apple defect inspection, over 98% of good apple detection rate is achieved. However, commercially available apple sorting and inspection machines cannot effectively solve the stem-calyx problems involved in automatic apple defects detection. In this study, a dual-spectrum NIR/MIR sensing method is applied. This technique can effectively distinguish true defects from stems and calyxes, which leads to a potential solution of the problem. The results of this study will advance the technology in fruit safety and quality inspection and improve the cost-effectiveness of fruit packing processes

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

Automated early plant disease detection and grading system: Development and implementation

As the agriculture industry grows, many attempts have been made to ensure high quality of produce. Diseases and defects found in plants and crops, affect the agriculture industry greatly. Hence, many techniques and technologies have been developed to help solving or reducing the impact of plant diseases. Imagining analysis tools, and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and Volatile Organic Compound (VOC) profiling techniques to detect early symptoms of diseases and defects of plants, fruits and vegetative produce. These disease detection techniques can be further categorized into two main groups; preharvest disease detection and postharvest disease detection techniques. This thesis aims to introduce the available disease detection techniques and to compare it with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this thesis incorporates the use of image analysis tools and k-means segmentation to implement a preharvest Offline and Online disease detection system. The Offline system to be used by pathologists and agriculturists to measure plant leaf disease severity levels. K-means segmentation and triangle thresholding techniques are used together to achieve good background segmentation of leaf images. Moreover, a Mamdani-Type Fuzzy Logic classification technique is used to accurately categorize leaf disease severity level. Leaf images taken from a real field with varying resolutions were tested using the implemented system to observe its effect on disease grade classification. Background segmentation using k-means clustering and triangle thresholding proved to be effective, even in non-uniform lighting conditions. Integration of a Fuzzy Logic system for leaf disease severity level classification yielded in classification accuracies of 98%. Furthermore, a robot is designed and implemented as a robotized Online system to provide field based analysis of plant health using visible and near infrared spectroscopy. Fusion of visible and near infrared images are used to calculate the Normalized Deference Vegetative Index (NDVI) to measure and monitor plant health. The robot is designed to have the functionality of moving across a specified path within an agriculture field and provide health information of leaves as well as position data. The system was tested in a tomato greenhouse under real field conditions. The developed system proved effective in accurately classifying plant health into one of 3 classes; underdeveloped, unhealthy, and healthy with an accuracy of 83%. A map with plant health and locations is produced for farmers and agriculturists to monitor the plant health across different areas. This system has the capability of providing early vital health analysis of plants for immediate action and possible selective pesticide spraying

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow