Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

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Electrogastrograms (EGG) are electrical signals originating from the digestive system, which are closely correlated with its mechanical activity. Electrogastrography is an efficient non-invasive method for examining the physiological and pathological states of the human digestive system. There are several factors such as fat conductivity, abdominal thickness, change in electrode surface area etc, which affects the quality of the recorded EGG signals. In this work, the effect of variations in the contact area of surface electrodes on the information content of the measured electrogastrograms is analyzed using Rényi entropy and Teager-Kaiser Energy (TKE). Two different circular cutaneous electrodes with approximate contact areas of 201.14 mm 2 and 283.64 mm 2 , have been adopted and EGG signals were acquired using the standard three electrode protocol. Further, the information content of the measured EGG signals were analyzed using the computed values of entropy and energy. Results demonstrate that the information content of the measured EGG signals increases by 6.72% for an increase in the contact area of the surface electrode by 29.09%. Further, it was observed that the average energy increases with increase in the contact surface area. This work appears to be of high clinical significance since the accurate measurement of EGG signals without loss in its information content, is highly useful for the design of diagnostic assistance tools for automated diagnosis and mass screening of digestive disorders.

Electrogastrograms (EGG) are electrical signals generated by the muscles of the stomach and the features of these signals can be used to diagnose several digestive disorders. Entropy is a measure of the disorder associated with a system and hence is a measure of complexity of the system. In medical diagnostics, entropy has proved to be an efficient feature for discriminating the normal and abnormal states of biological systems. In this work the EGG signals have been obtained from normal and abnormal subjects having different digestive abnormalities (diarrhea, vomiting and stomach ulcer), from a local hospital. Further, the Tsallis entropy associated with the collected signals has been analyzed. Results demonstrate that the Mean Tsallis Entropy (MTE) (with α=0.5) of the EGG signals obtained from normal individuals (MTE=313.861) is high when compared to the individuals having diarrhea (MTE=278.0259), vomiting (MTE=105.1278) and stomach ulcer (MTE=-839.201). Since, entropy is the complexity associated with the signal, it is found that the complexity of the normal EGG signals is high when compared to the abnormal EGG signals. This work appears to be of high clinical relevance, since feature extraction from EGG signals is highly useful for diagnosis of digestive abnormalities.

Background Electrogastrography is a noninvasive electrophysiological procedure used to measure gastric myoelectrical activity. EGG methods have been used to investigate the mechanisms of the human digestive system and as a clinical tool. Abnormalities in gastric myoelectrical activity have been observed in subjects with diabetes. Objective The objective of this study was to use the electrogastrograms (EGGs) from healthy individuals and subjects with diabetes to identify potentially informative features for the diagnosis of diabetes using EGG signals. Methods A total of 30 features were extracted from the EGGs of 30 healthy individuals and 30 subjects with diabetes. Of these, 20 potentially informative features were selected using a genetic algorithm–based feature selection process. The selected features were analyzed for further classification of EGG signals from healthy individuals and subjects with diabetes. Results This study demonstrates that there are distinct variations between the EGG signals recorded from healthy individuals and those from subjects with diabetes. Furthermore, the study reveals that the features Maragos fractal dimension and Hausdorff box-counting fractal dimension have a high degree of correlation with the mobility of EGGs from healthy individuals and subjects with diabetes. Conclusions Based on the analysis on the extracted features, the selected features are suitable for the design of automated classification systems to identify healthy individuals and subjects with diabetes.