Expert system design and architecture for farming sector

Maurya, Balmukund; Beg, Mohd. Rizwan; Mukherjee, S.

doi:10.1109/cict.2013.6558053

Cited by 7 publications

(3 citation statements)

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“…ESs have shown increasing development over the past 30 years and currently are applied to almost all human activities covering medicine [8], [9], agriculture [10], [11], aerospace [12], all the engineering technical areas, including power electronics and drives [13], [14], and especially robotics [15], [16]. There are also some ES applications in the pulp and paper industry [17], [18].…”

Section: Essmentioning

confidence: 99%

Expert System for the Detection of Condensate Accumulation Inside Dryer Cylinders During Section Starting

Ramírez

Valenzuela

Lorenz

2015

IEEE Trans. on Ind. Applicat.

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The presence of condensate inside the dryer cylinders of a paper machine produces a significant increase in the power required to accelerate the section, risking a drive overload and/or section shutdown. In previous works, the authors developed a robust estimation algorithm for the condensate power and a generalized correlation for the estimation of condensate loads in industrial dryer cylinders up to 55 mm of condensate rim. This paper presents and evaluates a rule-based expert system (ES) capable of detecting the presence and amount of condensate in up to three groups of cylinders with different condensate loads. The ES can give an early warning message to the operators and determine the condensate load and number of cylinders within each group. Evaluation is done using a combined condensate load for a fictitious 1500-mm eight-cylinder dryer section. Results show the capacity of the developed ES to give an early warning as well as a correct estimation of the condensate loads and number of cylinders involved.

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Section: Essmentioning

confidence: 99%

Expert System for the Detection of Condensate Accumulation Inside Dryer Cylinders During Section Starting

Ramírez

Valenzuela

Lorenz

2015

IEEE Trans. on Ind. Applicat.

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“…Agricultural IOT technology is a kind of network [11], by the radio frequency identification (RFID) technology, infrared sensors, laser scanner, GPS global positioning system information sensing equipment, according to the agreement, which combine the Internet with any products to implement the information communication and exchange, and to realize intelligent identification, location, tracking, monitoring and management. The basic features of IOT are comprehensive perception, reliable transmission, intelligent processing.…”

Section: Agricultural Iot and Precision Agriculturementioning

confidence: 99%

Monitoring and Discrimination of Plant Disease and Insect Pests based on agricultural IOT

Wang¹,

Wang²,

Zhang³

et al. 2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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“…This would make farmer convenient to know about his soil and choose the better crop. Also, the suggested nutrients or fertilizers would help to enhance the crop growth and yield [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence solution for crop recommendation

N¹,

Choudhary²

2022

IJEECS

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Agriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IOT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model.

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Expert system design and architecture for farming sector

Cited by 7 publications

References 0 publications

Expert System for the Detection of Condensate Accumulation Inside Dryer Cylinders During Section Starting

Expert System for the Detection of Condensate Accumulation Inside Dryer Cylinders During Section Starting

Monitoring and Discrimination of Plant Disease and Insect Pests based on agricultural IOT

An artificial intelligence solution for crop recommendation

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