Classification of nutrient deficiencies in rice crop using denseNet-BC

2021 ◽  
Author(s):  
R. Sathyavani ◽  
K. JaganMohan ◽  
B. Kalaavathi
Keyword(s):  
Rice Crop ◽  
Nutrient Deficiencies

Adoption Status of Direct Seeded Rice Technology by the Farmers of Punjab

2022 ◽  
Vol 58 (1) ◽  
pp. 76-80
Author(s):  
Ramandeep Kamboj ◽  
Dharminder Singh ◽  
Lakhwinder Kaur
Keyword(s):  
Rice Crop ◽  
New Technique ◽  
Nutrient Deficiencies ◽  
Climatic Regions ◽  
Transplanted Rice ◽  
Direct Seeded ◽  
Recommended Practices ◽  
A New Technique ◽  
Paddy Cultivation

Direct-seeded rice (DSR) is a possible alternative to conventional puddled transplantedrice, where rice crop is seeded directly in non-puddled fields. The study was conducted toascertain the adoption status of recommended practices of DSR. Personal interviews wereconducted to collect data from randomly selected 210 farmers from three districtsrepresenting different agro-climatic regions of Punjab. Study revealed that selected farmershad adopted DSR on 49.20 per cent of their total area under paddy cultivation. Slightlyhigher number of selected farmers had adopted Tar-Wattar, a new technique of DSR overdry soil method. Only 18.52 per cent respondents had applied first irrigation as perrecommendations of Tar-Wattar method, whereas majority (67.59%) had applied firstirrigation before 21 days. 26.67 per cent farmers had adopted most suitable variety PR-126 and sown it at recommended time. However, in case of other varieties, majority offarmers did not follow recommended time of sowing. A smaller number of farmers haveapplied zinc, iron and sulphur fertilizers to ameliorate micro nutrient deficiencies. Averageyield of others varieties and PR 126 were found to be nearly identical, but due to lessercost of cultivation in DSR as compared to transplanted rice, adopter farmers’ fetched highernet return.

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

scholarly journals A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam

2013 ◽  
Vol 6 (1) ◽  
pp. 135-156 ◽  
Author(s):  
Nguyen-Thanh Son ◽  
Chi-Farn Chen ◽  
Cheng-Ru Chen ◽  
Huynh-Ngoc Duc ◽  
Ly-Yu Chang
Keyword(s):  
Time Series ◽  
Mekong Delta ◽  
Rice Crop ◽  
Crop Monitoring ◽  
Modis Data

scholarly journals Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology

2021 ◽  
Vol 11 (12) ◽  
pp. 5640
Author(s):  
Elena Najdenovska ◽  
Fabien Dutoit ◽  
Daniel Tran ◽  
Antoine Rochat ◽  
Basile Vu ◽  
...  
Keyword(s):  
Stress Responses ◽  
Computation Time ◽  
Plant Health ◽  
Nutrient Deficiencies ◽  
General Stress ◽  
Abiotic Stressors ◽  
Crucial Component ◽  
Electrophysiological Signals ◽  
Plant Electrophysiology

Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health.

scholarly journals Detection and classification of nutrient deficiencies in plants using machine learning

2021 ◽  
Vol 1850 (1) ◽  
pp. 012050
Author(s):  
Anu Jose ◽  
S Nandagopalan ◽  
Vidya Ubalanka ◽  
Dhanya Viswanath
Keyword(s):  
Machine Learning ◽  
Nutrient Deficiencies

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

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