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Acetylcholinesterase (AChE), a widely used enzyme for inhibition-based biosensors in pesticide residues detection, lags due to multiple-step operation, time-consuming incubation and reactivation/regeneration steps. Herein, this endeavour reports the development of Organophosphate Hydrolase (OPH), which has functional superiority over the AChE and explored in on-spot biosensing device for organophosphate pesticide residue detection in fruits and vegetables. The organophosphate degrading enzyme OPH is expressed from the ‘opd’ gene through biotechnological tools. The OPH exhibited its best activity at pH 8.0 and subsequently thermal inactivation over 37 °C. The activity of the purified OPH enzyme was found 2.75 U mL-1 at λmax 410 nm. Furthermore, the developed OPH is integrated into 96 well plate format with our previously reported UIISScan 1.1, an advanced imaging array technology based field-portable high-throughput sensory system. The developed biosensor revealed a linear range from 100 ng mL-1 to 0.1 ng mL-1 for detection of organophosphate pesticide residues with a negative slope i.e. y = 235.678x (ng mL-1) – 62.8725 with R2 = 0.99991 and n = 23. Moreover, the applicability of the developed biosensor was tested for market available fruits and vegetables. This is the first-ever reported OPH mediated on-spot biosensing device for pesticide residue detection in fruits and vegetables to the best of our knowledge.

The paper has examined on the non destructive assessment of potatoes using a piezo based sensor. In assessing the freshness of the product, there are different research reports, but surface firmness is an excellent indicator and is used extensively in practice. The sensor is used as a vibration sensor where the vibration patterns are recorded and analyzed in frequency domain and then the quality parameters are displayed accordingly. It is found that dry matter is related with the firmness of potato tubers which also converts itself to starch content depending on time and storage of potato tubers as firmness is very useful for processing industry. With some minor software modifications it can be adopted for other vegetables as well.

The colorimetric sensing technology has evolved into an essential tool for high-throughput analysis including portability and cost-effectiveness among available biomedical and agricultural screening approach. In this endeavor, the objective of work is to focus on the development of a field-portable instrument based on an Uniform Illumination Imaging System (UIIS), which will facilitate the colorimetric biochemical sensing. The developed field-portable, wavelength independent UIIS has been exploited for (a) rotavirus detection using commercial enzymatic immunoassay based microplate kit; (b) pesticide residue detection and quantification; The proposed system exhibited a good correlation in comparison to another two conventional techniques, i.e., multi-plate reader (r = 0.9991938) and LC-MS/MS (r = 0.998877399) with a short analysis time of 5 min for 95 test samples. Moreover, the feasibility of UIIS system has also been explored as field-portable enzyme-linked immunosorbent assay (ELISA) plate reader. By incorporating the Mahalanobis distance calculation, the advanced algorithm has been investigated and developed to analyze the data. The overall dataset was transformed into a matrix format to give a good correlation with a conventional plate reader, i.e., r = 0.915389612. Internet of things (IoT) enabled decision support system can be exploited by using big data analytics. Finally, test results can be shared with concerned stakeholders and the remote users. Thus, the developed UIIS will help to identify potential public health threats expeditiosly compared to conventional time consuming process of sample submission to the laboratory for analysis.

Bruising on apples is mainly caused due to mechanical damage during harvesting and postharvest journey to the supermarket. Apple's bruise damage reduces the quality of apple as well as market price. This paper proposes an ultrasonic signal analysis technique to classify bruised and unbruised apples. An ultrasound signal with 100 kHz center frequency has been applied on the apple surface and the response signal has been recorded in reflectance mode. Principal Component Analysis (PCA) has been applied to the acquired response signals to reduce the dimensionality of the dataset. The first fifteen PCA components that contain more than 99% information have been used as the working dataset for classification. Then, the Support Vector Machine (SVM) classifier has been applied to this reduced dataset to classify bruised apple. SVM is trained with the training dataset that consists of data from both bruised and unbruised apples. The model developed after SVM training, is used to classify the test dataset. The classification result shows promising accuracy in the tune of 98%. This ultrasonic technique can endow with a new paradigm for automatic non-destructive identification of bruise-damaged apples.

Distinctiveness, Uniqueness and Stability (DUS) testing is a standard practice to establish a new variety of crop. Color measurement plays a major role in DUS testing. Color of plant parts like leaf, flower, fruit and stem are very important factors to establish the uniqueness of any candidate variety. To measure the color or to describe a color objectively by some name or code, Royal Horticultural Society (RHS) color chart is used by the DUS tester. RHS color chart is available in market. In manual process of color measurement, samples are placed under the porthole of the selected color-field of the RHS color chart to find the best matched color based on the visual perception of the person being. Visual evaluation is a tricky process as it depends on so many factors like lighting condition of the environment, gender, age, eyesight of the evaluator etc. Also, selecting of the proper color-field from the color fan of RHS color chart is fishy and it may lead to a biased result. In this paper, it is proposed to use an intelligent machine vision technology which will accurately and objectively measure color of plant parts in quick time. A database of colorimetric values of all RHS colors was used as a reference. Digital image processing and analysis algorithms were applied on the images of plant part to extract the color information using RGB, CIELab and CIELCh color models. Extracted color information was compared with the available color values in the reference database of RHS color chart using Euclidean distance, Chebyshev distance and Mahalanobis distance. These distance values were used to determine the top five matching color for each color model. Top priority was given to the color with minimum distance value. It was observed that CIELab color model using Mahalonobis distance was the most excellent model for color comparison while DUS testing. This technology may definitely help the DUS tester to accurately describe the color of plant parts in quick time.

Silk cocoon is the one and only raw material for silk industry. In cocoon trading, price of the cocoon is determined by guessing the silk content within it. Human expert visually inspects the cocoon by its shape, size, color, etc., and feels the toughness of the cocoon by pressing with thumbs. This process is subjective and varies from person to person. Invasive techniques are there to estimate the silk content in cocoons, but those techniques are time-consuming, expensive, laborious, laboratory-oriented, and difficult to implement on large scale. So, it is essential to develop a methodology which will be able to estimate the silk content in cocoons using noninvasive manner. This paper proposes a nondestructive X-ray imaging technique to estimate the silk content in cocoon. The technology applies advanced image processing followed by appropriate data analysis techniques. Different significant features from the X-ray image were extracted at first. The aim of the project was to develop a suitable mathematical model to estimate the silk content in the cocoon. Three neural network models, namely general regression neural network (GRNN), radial basis function neural network (RBFNN), and feed-forward back-propagation neural network (FFBPNN), were studied under this research. A comparative study shows that the general regression neural network (GRNN) provides the best performance with a reasonable accuracy of 85%.

Physical and biochemical attributes are commonly used for characterization of rice. The physical attributes are related to the quantification of size, shape, colour and texture of the rice grains. Biochemical attributes are assessed from cooking and eating characteristics of rice and are termed like alkali spreading value (ASV), amylose content (AC), gel consistency (GC), grain elongation etc. Estimation of biochemical attributes are often time consuming and require meticulous effort for sample preparation, storage and manual measurement. The gelatinization temperature (GT) is related to Alkali spreading value of rice and is partly associated with the amylose content of the starch. GT has a negative correlation with cooking temperature of rice. In this paper image analysis technique has been proposed for discrimination of rice. A portable flat bed scanner has been used as the imaging device and image analysis software has been developed to measure the rate of dispersion during ASV testing. This machine vision technique is a faster and effective way to determine the ASV. The results obtained are promising towards this new approach for objective estimation of ASV.

The colorimetric sensing technology has evolved into an essential tool for high-throughput analysis including portability and cost-effectiveness among available biomedical and agricultural screening approach. In this endeavor, the objective of work is to focus on the development of a field-portable instrument based on an Uniform Illumination Imaging System (UIIS), which will facilitate the colorimetric biochemical sensing. The developed field-portable, wavelength independent UIIS has been exploited for (a) rotavirus detection using commercial enzymatic immunoassay based microplate kit; (b) pesticide residue detection and quantification; The proposed system exhibited a good correlation in comparison to another two conventional techniques, i.e., multi-plate reader (r = 0.9991938) and LC-MS/MS (r = 0.998877399) with a short analysis time of 5 min for 95 test samples. Moreover, the feasibility of UIIS system has also been explored as field-portable enzyme-linked immunosorbent assay (ELISA) plate reader. By incorporating the Mahalanobis distance calculation, the advanced algorithm has been investigated and developed to analyze the data. The overall dataset was transformed into a matrix format to give a good correlation with a conventional plate reader, i.e., r = 0.915389612. Internet of things (IoT) enabled decision support system can be exploited by using big data analytics. Finally, test results can be shared with concerned stakeholders and the remote users. Thus, the developed UIIS will help to identify potential public health threats expeditiosly compared to conventional time consuming process of sample submission to the laboratory for analysis.

This paper proposes a non-destructive technique for silk content estimation in cocoons. The price of a cocoon is determined by the silk content which is determined manually by visual inspection or feeling the toughness of the cocoon shell. The above methods are subjective, non-repeatable and prone to human error. With such non-transparent conventional methods of silk estimation, the buyers and sellers are unhappy over any transaction. Our proposed non-destructive technique uses soft x-ray image analysis technique backed up by soft computing algorithm to estimate silk content. Advance image processing and analysis techniques have been applied to extract morphological features from the x-ray images of the cocoons and features are fed to GRNN to estimate the silk content. Total 594 tasar cocoons have been analyzed with the developed solution and the results have been validated with human experts. Accuracy of the system for silk content estimation has been calculated as more than 85%.

Quality of cocoons determined by its silk content which is directly related with the market value. Silk is broadly used in textiles and it is costly too. Farmers are selling the cocoons in a bulk with an average market value to the yarn producers. Presently, the quality of cocoons is verified by visualizing the color, size, shape and feeling its solidity on pressing by fingers of our hand. These manual inspections sometimes create dissatisfaction among the buyers and sellers. Sometimes, the sellers (farmers) are duped by the clever buyers. In other method, the silk content is estimated by taking the average raw cocoon shell weight after cutting the cocoon and removing the pupa from it. This approach is destructive, time consuming, expensive and laborious also. In this paper, X-ray imaging technique has been explored to estimate the silk content and determine the quality of cocoons. Firstly the images of the cocoons are captured using standard X-Ray imaging setup. Then the images are enhanced using digital image processing techniques. Finally, different dimensional features are extracted using image analysis techniques. A new method for estimation of the silk content has been proposed using the GRNN (General Regression Neural Network). Quality of the cocoons has been evaluated using unsupervised artificial neural network technique known as SOM (Self Organizing Map) which produces the different classes of quality grades of cocoons. In this experiment, we have considered five classes — good, medium, bad, dead pupa and un-identified quality. Total 49 no of cocoons have been used for the experimentation. The result shows that using GRNN the estimation of silk content is quite helpful with a fair level of accuracy. Using SOM technique, quality of cocoons has been determined and the result is validated with the manual inspection method. Both this approach of estimating the silk content and determining the quality of cocoons opens new possibilities in the field of automatic, non-destructive technique for price appraisal of cocoons.

In spite of the availability of natural resources and traditional skills, Tasar sericulture in India is stagnating due to frequent outbreak of a number of diseases. The most common and deadliest among all is Pebrine disease caused by a microsporidian parasite Nosema sp. Infections of the disease range from chronic to highly virulent and can result in complete loss of crop. The disease has become increasingly more and more complex as more number of microsporidian strains infecting silkworms is being identified. Therapeutic methods to control the disease at commercial scale have so far been proven to be ineffective. As of now, preventive methods are generally followed to restrict the disease below the danger threshold. As the disease is trans-ovarially transmitted hence the common method is to eliminate primary infection at the egg stage by testing the body fluid of the egg laying moths under microscope. If the tissues are found free of infection, then only the corresponding eggs are distributed amongst the villagers pursuing sericulture. Currently the entire process is manual, time and labour intensive. Many a time human error also creeps in leading to outbreak of the disease. This paper proposes automation of the disease detection process by capturing photo-micrographic images and classifying spores using digital image analysis technique thereby improving productivity and accuracy of this process. The proposed solution has been tested in the tasar grainages and the software results have been validated with the human experts. The accuracy of correct identification of Pebrine spores has been found as 87%.

Quality of Tasar fabric demands uniform coloured silk yarn during weaving. But, the variation of yarn colour depends on various natural factors like eco-race and feeding of silk worms, weather conditions etc and other production factors. So, silk yarns need to be sorted after production. At present, yarns are sorted manually by a group of experts which is subjective in nature. Again, due to lustrous nature of silk yarn, it reflects light and therefore it is difficult to ascertain the exact colour manually. Slight variation in colour is difficult to detect manually but the market demands lots with perfectly uniformly coloured yarns within the lot though the inter-lot variation in colour is encouraged. So, there is need to develop a solution which can grade the silk yarn objectively, reliably and mimic the human perception. This paper proposes a new machine vision solution for automatic grading of silk yarn based on its colour. The system consists of an enclosed cabinet which encompasses of a low cost digital camera, uniform illumination arrangement, weighing module, mechanical arrangement for sample holding and a grading software which applies image analysis technique using CIELab colour model with rotational invariant statistical feature based hierarchical grading algorithm for colour characterization. Performance of the system has been validated with the human experts and accuracy has been calculated as 91%.

Tasar Silk is one of the most widely used luxurious material not only in India but in all over the world. Tasar cocoons are used as the raw material for silk production. In sericulture industry one of the most important perspectives is disease free egg production for formation of healthy cocoons. Out of the many different diseases, Pebrine is the most destructive one which spreads transovarially and makes huge loss in Tasar silk industry. As of now, the egg laying moths are inspected manually by field experts using a low cost student microscope with 675X magnification in day light. Problems with the present methodology are of many folds - lack of field experts in remote places, tedious and time consuming process in case of visual inspection, insufficient light in cloudy atmosphere, lack of authenticity due to manual inspection etc. In this paper we have proposed a motorized microscopic image analysis solution which overcomes the above shortcomings. Positioning of the slide has been controlled using 2 nos of stepper motors and focusing of microscope has been done by introducing another stepper motor. Slides are prepared by taking smear of mother moth from its lower part of the abdomen and mixing with medium concentration (0.5 gm/ 100 ml water) of K2CO3 solution. Slides are placed manually under the microscope and images are captured using a low cost digital camera placed on the eyepiece of the microscope. Image analysis software has been developed to identify the Pebrine using different morphological features. More than 200 images have been analyzed using developed solution & the results have been validated with the human experts. Accuracy of the Pebrine disease detection solution in case of Tasar moth has been calculated as 85%.

An important aspect of silkworm seed production is to ensure Pebrine disease free eggs. For that reason, the egg-laying moths are cut and their tissues are examined under microscope for presence of Pebrine spores in those tissues. If the tissues are found free of infection, then only the corresponding eggs are distributed amongst the villagers pursuing sericulture. Currently the entire process is manual, time and labor intensive. Many a time human error also creeps in leading to outbreak of Pebrine disease. This paper proposes automation of the Pebrine spore detection process by capturing photomicrographic images and classifying Pebrine spores using digital image processing technique thereby improving productivity and accuracy of this process. Captured RGB image has been enhanced by image enhancement process to get better processing result in further steps. Local threshold based segmentation technique has been applied to segment the foreground objects. The segmented foreground objects have been labeled individually by a stack-based connected-component labeling technique. Then advanced binary morphological technique based feature detection procedures have been performed to remove the unwanted noise and non-Pebrine objects and to extract various feature parameters of filtered Pebrine objects. Initially, more than 200 images have been analyzed using developed solution & the results have been validated with the human experts. Laboratory experiments found the accuracy of detection in the tune of 75%.

The color of Tasar silk yarns is determined by a number of production factors, any slight variation in any one of these factors lead to variation in color of the yarn produced. At the present production technology, it is difficult to produce yarns of uniform color at the producers' level, but once produced, those yarns can be sorted based on its color. The important characteristic of tasar silk yarn is its lustrous nature, it reflects light, thus difficult to ascertain the exact color manually. Slight variation in color is difficult to detect manually but the market demands lots with perfectly uniformly colored yarns within the lot though inter-lot variation in color is encouraged. So, Yarn separation based on the color is highly subjective and the process of manually separation of color is tedious and monotonous also. Also, it requires expert manpower, which may not be available in the remote villages in all cases. So, there is a need to develop an instrument, which can easily grade the yarns based on the color. This paper proposes automation of the silk yarn grading process by capturing images and classifying the silk yarns using digital image processing based color analysis technique thereby improving productivity and accuracy of this process. CIELCh color scale has been used for color analysis. Principle Component Analysis (PCA) shows the formation of inherent clusters in the image dataset. Color feature parameter based hierarchical grouping has been introduced here for silk yarn color grading. More than 2000 images have been analyzed using developed solution & the results have been validated with the human experts. Laboratory experiments found the overall accuracy of system in the tune of 91%.

Theaflavins (TF) and thearubigins (TR) are the important chemical compounds, which contribute to the colour and brightness of tea liquor. Estimation of TF and TR in black tea is generally done using a spectrophotometer. But, the analysis technique undergoes rigorous time consuming effort for sample preparation; also the operation of costly spectrophotometer requires expert manpower. To overcome above problems an electronic vision system (E-Vision system) based on image processing has been developed, which is faster, low cost, repeatable and can estimate the amount of Total Colour (TC), Brightness (TB), Theaflavins (TF) and TF/TR ratio for orthodox tea liquors. This paper describes the newly developed E-Vision system, experimental methods using orthodox black tea sample, data analysis algorithms and finally, the performance of the E-Vision system as compared to the results of traditional spectrophotometer. The data analysis is done using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). A correlation has been established between colour of tea liquor images and TC, TB, TR and TF/TR ratio.

Quality of black tea is generally assessed using organoleptic tests by professional tea tasters. They determine the quality of black tea based on its appearance (in dry condition and during liquor formation), aroma and taste. Variation in the above parameters is actually contributed by a number of chemical compounds like, Theaflavins (TF), Thearubigins (TR), Caffeine, Linalool, Geraniol etc. Among the above, TF and TR are the most important chemical compounds, which actually contribute to the formation of taste, colour and brightness in tea liquor. Estimation of TF and TR in black tea is generally done using a spectrophotometer instrument. But, the analysis technique undergoes a rigorous and time consuming effort for sample preparation; also the operation of costly spectrophotometer requires expert manpower. To overcome above problems an Electronic Vision System based on digital image processing technique has been developed. The system is faster, low cost, repeatable and can estimate the amount of TF and TR ratio for black tea liquor with accuracy. The data analysis is done using Principal Component Analysis (PCA), Multiple Linear Regression (MLR) and Multiple Discriminate Analysis (MDA). A correlation has been established between colour of tea liquor images and TF, TR ratio. This paper describes the newly developed E‐Vision system, experimental methods, data analysis algorithms and finally, the performance of the E‐Vision System as compared to the results of traditional spectrophotometer.

Orthodox black tea quality depends upon the amount of certain organic compounds present and out of these, theaflavins (TF) and thearubigins (TR) are the most important ones While TF is responsible for attractive golden colour, increased brightness and astringency in tea liquor, TR is reddish brown, reduces the brightness of tea liquor and contribute mostly for the ashy taste of the liquor with minor improvement in astringency. The rapid estimation of TF and TR thus may resolve the problem of certain uncertainty or ambiguity that may arise during quality assessment of tea by the tea tasters. In this paper, a new method for rapid measurement of concentration of TF and TR is described using a machine vision system taking images of tea liquor and employing artificial neural networks (ANN). The results show good correlation of estimated values of TF and TR with the actual concentrations obtained using ultraviolet-visible spectrophotometer (UV-VIS)