Journal Article
Permanent URI for this collectionhttps://ir.daiict.ac.in/handle/123456789/37
Browse
7 results
Search Results
Publication Metadata only Understanding the Influence of Film Thickness on rGO-Based Flexible Capacitive Leaf Wetness Sensors for In-Situ Agriculture Applications(IEEE, 01-07-2025) Yogi, Pooja; Yadav, Rohit; Kumari, Kusum; Borkar, Hitesh; Roy, Anil; Palaparthy, Vinay; DA-IICT, GandhinagarIntegrated plant disease management is pivotal in abating crop loss. For this purpose, leaf wetness sensors (LWSs) are widely used to measure the leaf wetness duration. This work focuses on fabricating the LWS on flexible polyimide substrates and understanding its sensor transfer characteristics using reduced graphene oxide (rGO) as the sensing film with varying thickness. For this purpose, three different concentrations, viz, 0.001 mg (Device A), 1 mg (Device B), and 10 mg (Device C) of rGO are dispersed in 0.5 mL deionized water, and these are drop-casted on the fabricated LWS. Subsequently, sensor properties such as response, recovery/recovery time, hysteresis, and temperature effects are studied. Initial laboratory readings demonstrate that the fabricated LWS response for devices A, B, and C is 607866%, 6541%, and 780%, sensing area wetness, respectively. Further, the response times for devices A, B, and C are 10, 15, and 6 s, respectively. Interestingly, the recovery times of devices A, B, and C are approximately 15, 16, and 2462 s, respectively. Further, it has been observed that over the temperature range of 30 °C–60 °C, the sensor response changes by 2%, 5%, and 17% for devices A, B, and C, respectively.Publication Metadata only Detection and localization of copy-move tampering along with adversarial attack in a digital image(Springer, 02-07-2025) Diwan, Anjali; Roy, Anil; DA-IICT, GandhinagarPublication Metadata only Experimental Investigation of Leaf Wetness Sensing Properties of MoS2 Nanoflowers-Based Flexible Leaf Wetness Sensor(IEEE, 01-02-2023) Khaparde, Priyanka; Patle, Kamlesh S; Agrawal, Yash; Borkar, Hitesh; Palaparthy, Vinay; Gangwar, Jitendra; Roy, Anil; Agrawal, Yash; Palaparthy, Vinay; Roy, Anil; Patle, Kamlesh S (202121017)To abate crop loss, it is important to explore the plant disease management systems, where leaf wetness sensors (LWS) are widely used. The leaf wetness duration (LWD) extracted from the LWS is related to plant diseases. In this work, we have fabricated the LWS on the polyamide flexible substrate where Molybdenum disulfide (MoS2) is used as the sensing film to explore the leaf wetness sensing mechanism. Further, we have passivated the MoS2�with the help of acrylic protective lacquer (APL) conformal coating (MoS2�+ APL), which reduce the interaction of the water molecules with the sensor. Lab measurements indicated that fabricated LWS on the flexible substrate with MoS2�and MoS2�+ APL as the sensing film offers a response of about ? 40 000% and ? 250%, respectively, at 500 Hz excitation frequency when the entire sensing area is filled with the water molecule. The response time of the MoS2�and APL-coated flexible sensor is about 180 s. Fabricated LWS sensors offer hysteresis of about � 4% in wetness. Further, we have identified that oxidation of the sulphur in the MoS2�plays an important role in the leaf wetness sensing mechanism. Furthermore, we understood that MoS2�when passivated with APL coating, the oxidation effect is reduced and the sensor response is negligible.Publication Metadata only Fractional Derivative Based TVD Smoothening and Baseline Correction for Extracting Leaf Wetness Duration From LW Sensor: A Novel Approach(IEEE, 28-11-2023) Agrawal, Yash; Gupta, Samaksh; Roy, Anil; Palaparthy, Vinay; Kumar, AhladOne of the driving factors leading to the modernization in the agriculture sector is the era of sensors-driven technologies. Annually, as reported by the Associated Chambers of Commerce and Industry of India, $500 billion of crops are lost due to pests and plant diseases in a country like India, where at least 200 million Indians go to bed hungry every night. For the detection of plant disease, the measurement of leaf wetness duration (LWD) values becomes a crucial step. This requirement of measuring LWD values led to the development of an in situ IoT-enabled LW sensor earlier. The same LW sensor was deployed for about four months, and data for the same were collected. Furthermore, for extracting LWD information, smoothing algorithms like total variation denoising (TVD) are applied. However, our novelty lies in introducing the order of fractional derivative (?) in an already existing TVD algorithm, which is varied from 1 to 2, and results are found to be satisfying. To get an effective baseline, we combined this algorithm with three baseline correction techniques: asymmetric least squares, improved asymmetric least squares, and asymmetrically reweighted penalized least squares (arPLS). The optimal range of ? lies in the range of 1.6 to 2 for getting the highest accuracy. This study demonstrates that our novel approach of integrating fractional derivatives into an existing TVD algorithm enhances its performance in identifying Leaf wetness events. The highest accuracy (i.e., the highest number of events detected) of 0.80 is found by total variation smoothing with the arPLS baseline correction technique.Publication Metadata only Investigating an Impact of Leaf Bending Radius and Angle for Flexible Leaf Wetness Sensor(IEEE, 01-03-2024) Khaparde, Priyanka; Patle, Kamlesh S; Borkar, Hitesh; Gangwar, Jitendra; Roy, Anil; Palaparthy, Vinay; DA-IICT, Gandhinagar; Patle, Kamlesh S (202121017)It is pivotal to monitor and examine the plant disease during in situ measurements to abate the crop loss. For this purpose, leaf wetness sensors (LWS) are widely used. However, for the LWS during in situ measurements, operational exposure is always a concern considering the plant growth at different stages. During the plant growth, the stem angle changes and even the leaf canopy bends either inward or outward due to environmental factors or physical trauma. Thus, LWS placed on the leaf canopy may produce erroneous results. In this letter, we have examined the effect of leaf bending radius (outward or inward) and angle (from 0� to 90�) on the flexible LWS fabricated on the polyamide substrates. LWS comprises of interdigitated electrodes (IDEs) having interelectrode spacing 0.05 cm. Fabricated LWS are 3.5 cm long and 1.5 cm wide in dimension. We have used the two LWS viz. one bare IDEs and another with molybdenum disulfide (MoS2) coated LWS. Lab experiments indicated that sufficient wetness remained on the bare IDEs and MoS2-coated IDEs till 40� and 70� of bending angle, respectively. Subsequently, when the LWS are bended outward or inward, bare IDEs and MoS2-coated IDEs retain water molecules till 0.7 and 1 cm, respectively, when bended from its initial length (3.5 cm).Publication Metadata only Evolution of Dew and Rain Water Resources in Gujarat (India) between 2005 and 2021(Preprint.org, 24-06-2024) Budhbhatti, Rupal; Muselli, Marc; Beysens, Daniel; Roy, Anil; Roy, Anil; DA-IICT, GandhinagarGlobal warming leads in general to a reduction in precipitations and non-rainfall water contributions such as dew. The present study, carried out in Gujarat (India) for 17 years between 2005 and 2021, shows however a rare increase of the rain falls and dew condensation, the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain was obtained from meteo stations. Dew was calculated from an energy model and meteo data from 15 meteo stations (14 in India and 1 in Pakistan) and compared to early and dedicated measurements in 13 sites. Maps of dew, rain and their relative contributions during the period are provided. They show that, while rain mainly occurs during the monsoon (June to September), dew forms during the dry season (October to April). This water resource was mapped over the Gujarat state of India by using a kriging process. According to the site, the dew/rain ratios can represent between 4.6% (Ahmedabad) to 37.2% (Jamnagar) on the whole period of 17 years. This positive evolution, which is observed since 2015-2017, is likely to continue in the future.Publication Metadata only CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection(IEEE, 21-03-2024) Diwan, Anjali; Roy, Anil; Roy, Anil; DA-IICT, GandhinagarAuthenticating digital images poses a significant challenge due to the widespread use of image forgery techniques, including copy-move forgery. Copy-move forgery involves copying and pasting portions of an image within the same image while applying geometric transformations to make the forged image appear genuine. Furthermore, additional processing techniques such as additive noise scaling, JPEG compression, and rotation can be employed to further conceal evidence of forgery. These factors contribute to the complexity of detecting and verifying the authenticity of digital images. The proposed work uses a combination of the CenSurE keypoint detection and a CNN architecture to detect and localize copy-move forgery in digital images. The use of CNN architecture allows the algorithm to update its learning via training data repeatedly, making it a data-driven approach. By combining keypoints with CNN features, the proposed approach can enhance the detection of copy-move forgery even in the presence of attacks such as geometrical transformations, scale, and rotation. Additionally, the proposed approach can effectively handle post-processing operations such as JPEG compression, additive noise, image blur, colour reduction, brightness change, and contrast adjustment. One important aspect of the proposed approach is its ability to handle images with different textures, including smooth and self-similar structural images with dense textures. The proposed approach can produce stable results in images with various attacks, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images. The proposed approach represents an important contribution to the field of multimedia forensics, providing an effective and reliable means of detecting and localizing copy-move forgery in digital images.