Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.

Ground Penetrating Radar (GPR) is a preferred non-destructive method used for the identification and localisation of subsurface targets. Hyperbolic signatures present in GPR B-Scans can provide valuable insights about the shape, material and the distance at which target objects are buried below the surface. This paper presents an innovative approach to enhance GPR data analysis, specifically focusing on hyperbola detection within GPR B-Scans. Preprocessing steps, such as dewow filtering, frequency filtering, and gain compensation, are used to improve GPR data quality. A comparative analysis is conducted on the performance of Canny, Sobel, and Scharr edge detectors in identifying hyperbolic signatures. By merging the Canny edge detection algorithm with the Column-Connection Clustering (C3) method, common limitations of conventional clustering, particularly their sensitivity to noise and outliers are addressed. Finally, the efficacy of the proposed method is evaluated by calculating the R-squared values of the fitted hyperbolas which was found to be > 0.93.

GPR (Ground Penetrating Radar) is a robust and effective device for identifying underground artefacts. Construction companies and civil engineers should be aware of the sizes of rebars and pipelines before and during construction work for various reasons. Most research efforts have typically concentrated on GPR signal analysis in the time domain, however recent studies have increasingly focused on analysis in the frequency domain. This paper proposes an artificial neural network (ANN) model for estimating the diameter of underground rods (solid) and pipes (hollow). GPR data captured in the time domain domain is transformed to the frequency domain using FFT, after which feature extraction is performed using ANN. An FPGA-based prototype GPR system is used to collect GPR A-Scan data for a variety of targets made of aluminium, stainless steel, rebar, and PVC. A mean absolute percentage error of 1.89% is achieved using the proposed model. The experimental results confirm the effectiveness of the proposed approach in extracting size-related information from GPR data.

A convolutional neural networks (CNN) model for predicting size of buried objects from ground penetrating radar (GPR) B-Scans is proposed. As a pre-processing step, Sobel, Laplacian, Scharr, and Canny operators are used for edge detection of the hyperbolic features. The proposed CNN architecture extracts high level signatures in the initial stages of the model and learns additional low-level features when the input data passes through the neural network to finally make an estimation of the required parameter. Artificially generated GPR B-Scans are used to train the model. The proposed method demonstrates good performance in predicting buried object size. Upon comparison, Scharr operator followed by a deep CNN model showed the best performance, having the minimum mean absolute percentage error of 6.74 when tested on new, unseen data.

Ground penetrating radar (GPR) is a preferred non-destructive method to study and identify buried objects in the field of geology, civil engineering, archaeology, military, etc. Landmines are now largely composed of plastic and other non-metallic materials, while archaeologists must deal with buried artefacts such as ceramics, pillars, and walls built of a range of materials. As a result, understanding the material properties of buried artefacts is critical. This study presents an ANN model for automatic classification of buried objects from GPR A-Scan data. The proposed ANN model is trained and validated using a synthetic dataset generated using gprMax. The model performs well in classifying three different object classes of aluminium, iron and limestone, while achieving an overall accuracy of 95%.

Ground Penetrating Radar (GPR) uses electromagnetic waves to detect objects beneath the earth’s surface. Even though signal processing plays a significant role in GPR performance, the quality of the acquired data is also dependant on the antenna and the associated electronic circuitry. Bow-tie antennas are a popular choice in GPR systems because of their lightweight design, planar structure and ultra-wideband characteristics. Recent advances in planar microstrip antenna design have thrown up lots of possibilities for this antenna type in GPR applications. In this paper, a comparative analysis of a planar microstrip antenna and a bow-tie slot antenna is presented. Both the antennas are designed for a centre frequency of 1.5 GHz, and are fabricated on Flame Retardant 4 (FR4) substrate. The planar microstrip antenna is fed by a microstrip line, whereas the co-planar waveguide feeding is used for the bow-tie antenna. The bow-tie antenna exhibits a measured bandwidth of ∼65%, with minimum return loss of -33.60 dB at 1.46 GHz. On the other hand, the microstrip antenna exhibits a return loss of -30.53 dB at its centre frequency of 1.51 GHz.

Soil moisture estimation is essential for understanding the water cycle and its impact on weather and climate. GPR based soil moisture estimation is non-invasive in nature and provides quicker results as compared to standard laboratory approaches. Deep learning algorithms have been shown to be an effective method for extracting characteristics from GPR data. To assess soil moisture, this paper offers a deep artificial neural network (ANN) model. The data set was created using finite-difference-time-domain (FDTD) simulation and is used to train and validate the ANN model. The suggested model predicts soil moisture from GPR A-Scans well, with R2 = 0.998 and mean average percentage error (MAPE) of 1.23.

This study presents the design a 400 MHz CPW-fed bow-tie slot antenna backed by a cavity. The application of UWB signal sources in ground penetrating radar (GPR) is widely recognised. The antennas are first modelled and simulated, and then tested using a VNA. The antenna has a high gain and very good (F/B) ratio. The antenna exhibits resonance at 401 MHz and 421 MHz and has a measured bandwidth of 47.5%. It has a realised gain of 6.68 dB at 400 MHz.

Traditional methods for classification of soil types are time consuming, invasive and expensive. A non-invasive method like ground penetrating radar (GPR) provides a suitable way to classify soil types based on its electromagnetic properties. Deep learning algorithms have proven to be an effective tool for features extraction of GPR data. A deep convolutional neural network (CNN) model for automatic classification of soil types is proposed. A synthetic dataset is created using gprMax and used to train and validate the proposed CNN model. The proposed model shows good performance in classifying 7 different soil types from GPR B-Scan images. Upon testing the model on new and unseen data, its accuracy is found to be 97%.

Identifying and classifying buried artefacts remains a major scientific and technological problem. Shape recognition will aid investigators in classifying buried items and narrowing down areas of interest. An artificial neural network (ANN) model for automated object shape classification using GPR data is developed in this paper. A synthetic dataset is developed using finite difference time domain (FDTD) simulation and utilised to train and evaluate the proposed ANN model. The model performs well in identifying three shapes of cylindrical, rectangular, and triangular objects.

Ground penetrating radar (GPR) uses electromagnetic (EM) wave to detect the subsurface objects. Interpretation and analysis of GPR signals are still challenging tasks as it requires skilled user (geologists in most cases). Particularly difficult is the prediction of the object sizes. This paper proposes a new method for predicting size of buried objects. First, standard scaling pre-processing techniques are used to optimise the B-Scan data. The features are then supplied to Random Forest (RF) and Support Vector Machine (SVM) classifiers to automatically predict the size of the buried object. The proposed feature based RF classifier shows similar performance in the accuracy of classification compared to SVM (Radial Basis Function kernel) system.

Assam is the highest producer of eri silk in the country with 3143 MT raw silk production [2].A total of 5,52,063 families are engaged in production of eri silk [3].Eri culture has always been a subsidiary occupation of the rural folk of Indo-Mongoloid and Tibeto-Burman races of the Brahmaputra valley.Communities like the Misings, Kacharis, Bodos, Mikirs, Rabhas, Karbis, Garos practice this culture during their leisure time and it helps to improve their economic condition.Though eri culture is practiced in almost all the districts of Assam, it is highly concentrated in the districts of Karbi Anglong, North Cachar Hills, North Lakhimpur, Demaji, Barpeta, Kokrajhar, Sibsagar, Darrang etc.The word 'Eri' derived from the word 'era' which means castor.The eri silkworm, Samia ricini Donovan (Lepidoptera: Satumiidae) is a holometabolous, polyphagous and multivoltine insect.It is one of the most exploited, domesticated and commercialized non-mulberry silkworms.It is very common in the Northeast region of India whichResearch