Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined.  Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks

ANTAGONISTIC POTENTIAL OF FLUORESCENT Pseudomonas AND ITS IMPACT ON GROWTH OF TOMATO CHALLENGED WITH PHTOPATHOGENS

This study focused on the antagonistic potential of fluorescent Pseudomonas in vitro, and its inoculation effect on growth performance of Lycopersicon esculentum in Fusarium oxysporum and Rhizoctonia solani infested soil. Biochemical characteristics of fluorescent Pseudomonas showed that all ten isolates were positive to catalase, amylase, gelatinase and siderophore production. While three isolates (Pf5, Pf6 and Pf9) were oxidase positive, nine isolates (Pf1, Pf2, Pf3, Pf4, Pf6, Pf7, Pf8, Pf9, and Pf10) were tolerant to 6.5% NaCl. Isolates Pf5 and Pf6 were resistant to all the test antibiotics; in contrast, the remaining eight isolates responded differently to different antibiotics. Isolates Pf5 and Pf6 were antagonistic against 14 bacterial species, and two pathogenic fungi (F. oxysporum and R. solani). Inoculation with fulorescent Pseudomonas Pf5 induced a significant increase in root and shoot length, and dry weight. Treatment of plants with either F. oxysporum or R. solani drastically reduced the root and shoot length and dry weight of the plant. However, in the presence of fluorescent Pseudomonas the adverse effect of the pathogens on growth of L. esculentum was alleviated.Cette \ue9tude a port\ue9 sur le potentiel antagonistique du Pseudomonas fluorescent, in vitro et les effets de son inoculation sur la performance en croissance du Lycopersicon esculentum dans le sol infest\ue9 par le Fusarium oxysporum et le Rhizoctonia solani . Les caract\ue9ristiques biochemiques du Pseudomonas fluorescent ont montr\ue9 que tous les dix isolats \ue9taient positives eu \ue9gard \ue0 la production de catalase, amylase, g\ue9latinase et sid\ue9rophore. Alors que trois isolats (Pf5, Pf6 and Pf9) \ue9taient oxidase positifs, neuf isolats (Pf1, Pf2, Pf3, Pf4, Pf6, Pf7, Pf8, Pf9, et Pf10) \ue9taient tolerant au 6.5% NaCl. Les isolats Pf5 et Pf6 \ue9taient r\ue9sistants \ue0 tous les test antibiotiques; au contraire, les huit isolats restants ont r\ue9pondu diff\ue9remment aux diff\ue9rents antibiotiques. Les isolats Pf5 et Pf6 \ue9taient antagonistiques contre 14 esp\ue8ces de bact\ue9ries, et deux champignons pathogeniques (F. oxysporum et R. solani). L\u2019inoculation avec Pseudomonas fulorescent Pf5 a induit une augmentation significative des raciness et de la longueur des tiges, ainsi que du poids sec. Le traitement de plants avec du F. oxysporum ou du R. solani ont radicalement r\ue9duit la longueur des raciness et tiges ainsi que le poids sec du plant. Cependant, en pr\ue9sence du Pseudomonas fluorescent, l\u2019effet adverse du pathog\ue8ne sur la croissance du L. esculentum \ue9tait allevi\ue9

Genetic variability, heritability and genetic advance for yield and yield components in watermelon (Citrullus lanatus Thunb.)

Field investigation was carried out to study the genetic variability, heritability and genetic advance and the variability studies showed significant differences among the thirty genotypes for all the thirteen characters. Yield per plant was maximum in CL 4 genotype collected from Atchirupakkam in Villupuram district. The characters viz., number of vines per plant, sex ratio, days to first female flowers, node number of first female flower, days to fruit maturity and number of fruits per plant were recorded the maximum in the same genotype. Genetic analysis indicated maximum phenotypic and genotypic coefficient of variation for single fruit weight and 100 seed weight. The characters viz., fruits diameter, flesh thickness, number of fruits per plant and yield per plant, recorded highest estimate of PCV and moderate estimation of GCV. The characters viz., number of seeds per fruits, flesh thickness, number of primary branches and fruit diameter recorded moderate estimate of PCV and GCV. Lower estimation of GCV was observed for sex ratio, fruit length and number of male and female flowers. High heritability (broad sense) was observed for 100 seed weight, number of seeds per fruit, single fruit weight, vine length, fruit diameter, fruit length, flesh thickness, number of male flowers,sex ratio, yield per plant, number of primary branches per plant, number of female flowers and number of fruits per plant. Based on mean performance, CL 4 followed by CL 22 and CL 10 were selected as the best genotypes in watermelon for the costal ecosystem, by virtue of their higher yield combined with desirable component characters

Mapping Rice Area in the Cauvery Delta Zone of Tamil Nadu Using Sentinel 1A Synthetic Aperture Radar (SAR) Data

Since remote sensing based crop inventory provides accurate and timely information as compared to the conventional survey methods of estimating area, Multi-temporal Sentinel 1A Synthetic Aperture Radar data was used for the estimation of rice area during Samba season 2022 in the Cauvery delta zone comprising Thanjavur, Thiruvaru, Mayiladuthurai, and Nagapatnam districts of Tamil Nadu. SAR data was preferred over optical satellite data due to excess cloud cover during cropping the major season in Tamil Nadu. Temporal back-scatter (dB) signature of rice crop was generated from the multi-temporal processed SAR data utilizing the modules of a fully automated MAPscape software aiding the discriminating of the crop from others. The signatures revealed that the dB levels to be the lowest during agronomic floods, reached the highest during maximum tillering stage and started declining thereafter. Multi-temporal feature extraction module of Mapscape was used to estimate rice area and validated for accuracy using ground truth data collected during survey. A total of 3.05 lakh ha of rice area was estimated with an overall accuracy of 90.8 % and 0.82 kappa coefficient. Largest area of 1.12 lakh ha was recorded in Thanjavur followed by Thiruvarur and Mayiladuthurai with 0.95 and 0.51 lakh ha respectively

Crop Diversification Assessment in Tank Ayacut Areas of Lower Palar Sub-Basin of Chengalpattu District, Tamil Nadu, India Using Geo-Spatial Techniques

For the assessment of crop diversification in the major tank Ayacut area of the Lower Palar sub-basin in Chengalpattu district of Tamil Nadu, research works were carried out using Sentinel 2 optical data by relating with ground truth data, to identify the crops in pixel-based classification and further classified the crops using Random Forest machine learning algorithms. The total area estimated under crop classification was 15767.97 and 28818.17 ha respectively for the summer seasons of 2018 and 2021. Since, the summer season experiences high crop diversification. The water spread area and water volume of tanks estimated were 612.31 and 1177.89 ha and 6,39,248 and 14,06,056 m3 respectively for 2018 and 2021. The accuracy assessment of ground truth points by confusion matrix reveals an overall classification accuracy of 96.8% (2018) and 94.9 % (2021) with kappa scores of 0.96 and 0.94 respectively. The crop diversification assessments were estimated using the Simpson Index of Diversity and values of 0.63 and 0.68 were accounted for in 2018 and 2021 respectively. The diversified pattern of crops is significantly correlated with tank water availability which increased the cropping area in 2021 as confirmed by the Crop Diversification factor

Impact of Drone Spraying of Nutrients on Growth and Yield of Maize Crop

This study aimed at utilizing unmanned aerial vehicle in place of a conventional hand sprayer for the smart delivery of agricultural inputs especially crop nutrients. A field experiment was conducted in the farms of Agricultural Research Station, Bhavanisagar, Tamil Nadu Agricultural University. There were nine treatments which were replicated thrice in a randomized block design. The treatments include NPK 19:19:19 along with liquid micronutrient, humic acid, and TNAU Maize maxim at two intervals viz., 50% Tasselling, and Cob filling stage. These nutrients were applied as foliar spray through battery operated and fuel operated drones and were compared with knapsack hand sprayer. Biometric observations such as plant height, leaf area, dry matter accumulation and yield parameters such as cob yield and number of grains per cob were observed during the critical crop growth stages. Foliar application of nutrients through drones had a significant influence on the growth and yield of maize crop. TNAU Maize maxim applied using the fuel-operated drone with an atomizer nozzle (T7) @ 30 lit/ac spray fluid recorded the maximum biometric and yield attributes than other treatments. Improved biometric attributes like plant height of 261.2 cm and 270.32 cm, LAI of 4.14 and 5.15, and DMP of 12354 kg/ha and 18564 kg/ha at 60 DAS and 90 DAS, respectively was recorded with drone spray. It also resulted in a grain and stover yield of 7195 kg/ha and 10942 kg/ha, respectively than hand sprayer

Generating Soil Parent Material Environmental Covariates Using Sentinel – 2A Images for Delineating Soil Attributes

Soil mapping procedures typically involve the combination of possible soil-forming SCORPAN factors. Among the factors, parent materials/ mineralogy has been considered important for the soil classification besides the Organisms (O) and Relief (R). Inclusion of the parent material covariate for the Digital soil mapping involves implication through geological maps, spectral derivatives and predictive modelling. In this study, the most prominent parent materials identified were derived using the spectral indices formulated based on the Sentinel – 2A multispectral information. While considering the coarse spatial resolution constraints of the existing Landsat -8 bands that may limit certain applications, Sentinel-2 images were used for the indices derivation. The generated mineral maps can support the digital soil mapping of the soil attributes at different spatial scales