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Shukla, Arvind Kumar
- Estimation of Soil Properties and Leaf Nutrients Status of Oil Palm Plantations in an Intensively Cultivated Region of India
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Authors
Affiliations
1 ICAR-Indian Institute of Soil Science, Bhopal 462 038, IN
2 ICAR-Indian Institute of Oil Palm Research, West Godavari 534 450, IN
1 ICAR-Indian Institute of Soil Science, Bhopal 462 038, IN
2 ICAR-Indian Institute of Oil Palm Research, West Godavari 534 450, IN
Source
Current Science, Vol 117, No 3 (2019), Pagination: 498-502Abstract
Oil palm (Elaeis guineensis Jacq.) is cultivated in several countries of the world. The information pertaining to soil properties and status of leaf nutrients in oil palm plantations (OPP) is essential for proper nutrient management to obtain higher yield of the crop. The study, therefore, was undertaken by conducting a survey of OPP in west Godavari district, India and collecting 306 soil samples and 153 leaf samples. Collected samples (soil and leaf) were analysed for different parameters after their processing. The studied soil parameters (soil pH, electrical conductivity, organic carbon, available phosphorous, available potassium, exchangeable calcium, exchangeable magnesium, available sulphur and available boron) in surface (0 to 20 cm) and sub-surface (20 to 40 cm) soil varied widely. The soil parameters had CV values from 7.13% to 80.7%. The concentrations of nitrogen (N), phosphorus (P) and potassium (K) in leaf samples were 0.62–3.97%, 0.04–0.26%, and 0.34–1.38% respectively. Whereas, the concentrations of calcium (Ca), magnesium (Mg), sulphur (S) and boron (B) were 0.66–2.66%, 0.10–1.03%, 0.02–0.35% and 9.55– 119 mg kg–1 respectively. The norms and indices of Diagnosis and Recommendation Integrated System (DRIS) were obtained using various nutrient expressions. The leaf nutrient requirement order was B > Mg > K > N > P. The optimum concentrations of leaf nutrients were 1.57–2.63% for N, 0.08–0.16% for P, 0.48–0.88% for K, 0.25–0.71% for Mg and 22.6– 60.2 mg kg–1 for B. Information about soil nutrient status and nutrient requirement order and optimum leaf nutrient ranges can be used for effective management of nutrients in the OPP of the study region.Keywords
DRIS, Leaf Nutrient, Oil Palm, Soil Property.References
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- Transduction Based Deep Belief Networks Learning-Based Multi-Camera Fusion for Robust Scene Reconstruction
Abstract Views :32 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Applications, IFTM University, IN
2 Department of Computer Science, Apeejay Stya University, IN
3 School of Modern Media, University of Petroleum and Energy Studies, Dehradun, IN
4 Department of Computer Science and Engineering, KSR Institute for Engineering and Technology, IN
5 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, SA
1 Department of Computer Applications, IFTM University, IN
2 Department of Computer Science, Apeejay Stya University, IN
3 School of Modern Media, University of Petroleum and Energy Studies, Dehradun, IN
4 Department of Computer Science and Engineering, KSR Institute for Engineering and Technology, IN
5 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, SA
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3060-3065Abstract
In the realm of scene reconstruction, conventional methods often struggle with challenges posed by occlusions, lighting variations, and noisy data. To address these limitations, this paper introduces a Transduction-based Deep Belief Network (T-DBN) within a learning-based multi-camera fusion framework, offering robust scene reconstruction by effectively fusing data from multiple cameras and adapting to diverse conditions. Traditional scene reconstruction methods often struggle with challenging scenarios due to limitations in handling occlusions, lighting variations, and noisy data. The proposed T-DBN model overcomes these limitations by effectively fusing information from multiple cameras using a transduction scheme, allowing it to adapt to varying conditions. The network learns to decipher scene structures and characteristics by training on a diverse dataset. Experimental results demonstrate the superiority of the Proposed T-DBN in achieving accurate and reliable scene reconstruction compared to existing techniques. This work presents a significant advancement in multi-camera fusion and scene reconstruction through the integration of deep learning and transduction strategies.Keywords
Transduction, Deep Belief Networks, Multi-Camera Fusion, Scene Reconstruction, RobustnessReferences
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- Swarm Intelligence Embedded Data Mining for Precision Agriculture Advancements
Abstract Views :42 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, IN
2 Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, IN
3 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
4 Department of Computer Applications, IFTM University, IN
1 Department of Information Technology and Cognitive Systems, Sri Krishna Arts and Science College, IN
2 Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering, IN
3 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
4 Department of Computer Applications, IFTM University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3218-3223Abstract
The present study investigates the potential of Swarm Intelligence (SI) in driving breakthroughs in Precision Agriculture (PA). It focuses on the research of mining techniques to uncover novel insights and developments in the field of PA. Social informatics (SI) is an academic discipline that focuses on the examination of collective behaviour within both herbal and synthetic structures. In order to gather, analyse, and synthesise information, SI utilises self-sufficient mobile devices known as Autonomous Mobile Agents (AMAs). These entities refer to robotic and computational frameworks that engage in mutual interaction, facilitating the examination of collective intelligence. This essay examines the potential impact of utilising the System of International Units (SI) on enhancing the accuracy and precision of commodity production and control in the field of production agriculture (PA). It also highlights the existing advancements that have been achieved in this regard. This analysis examines possible uses of Swarm Intelligence in the Public Administration (PA) industry, as well as the challenges that need to be solved in order to enhance the efficiency and accuracy of PA operations.Keywords
Swarm Intelligence, Embedded Data Mining, Precision Agriculture, Machine Learning, Artificial Intelligence, Crop Yield.References
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