Neural Networks in Cultivation

Shankar, D. Ravi; Kodali, Anuradha; Beerla, Pradeep; Nimai, D.V.S.M.

doi:10.1109/itng.2007.133

Cited by 3 publications

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An intelligent system for predicting Thrips tabaci Linde pest population dynamics allied to cotton crop

Patil

Govardhan

Mytri³

2009

2009 2nd IEEE International Conference on Computer Science and Information Technology

View full text Add to dashboard Cite

The agricultural sector in India is up against a series of problems when it comes to increasing crop productivity. A number of successful researches have been carried out to discover productive agricultural practices to improve crop cultivation but despite their efforts, productivity achieved by most of the farmers has not been in upper-bound level. The prime reason stated globally for crop loss is Insect pests. An efficient pest management technique can be devised if we could predict in advance the occurrences of peak activities of a given pest. Researchers are undertaken to understand the pest population dynamics by employing analytical and other techniques on pest surveillance data sets. In this paper, we present an intelligent system for pest prediction in cotton crop with the aid of the data obtained from College of Agriculture, Raichur, India. We make an effort to understand population dynamics of Thrips tabaci Linde (Thrips) pest on cotton (Gossypium Arboreum) crop using neural networks by analyzing pest surveillance data. The Multi-layer perceptron neural network with back-propagation training algorithm is utilized in the design of the presented intelligent system. The results show that neural network system can be able to give results with a very high degree of accuracy and is best suited to build a prediction system. With the aid of this pest prediction system, the farming communities get more beneficiaries in crop productivity.

show abstract

An intelligent system for predicting Thrips tabaci Linde pest population dynamics allied to cotton crop

Patil

Govardhan

Mytri³

2009

2009 2nd IEEE International Conference on Computer Science and Information Technology

View full text Add to dashboard Cite

show abstract

Using Neuromodels for Evaluating and Determining Productivity Technical Processes

Leoshchenko

Oliinyk

Zaiko

et al. 2020

2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T)

View full text Add to dashboard Cite

A Prediction Model for Population Dynamics of Cotton Pest (Thrips tabaci Linde) using Multilayer-Perceptron Neural Network

Patil¹,

Mytri²

2013

IJCA

View full text Add to dashboard Cite

India's agricultural sector faces a series of problems when it comes to increasing crop productivity. Despite the efforts of researchers to discover productive agricultural practices, crop yield has not been the most pleasing, and one global reason stated for this poor crop yield is the insect pests. Predicting in advance the occurrence of peak activities of a given pest could enable the development of a suitable pest control mechanism that would initiate better production. Researchers have attempted to comprehend the pest population dynamics by applying analytical and other techniques on pest surveillance data sets. In this paper, An intelligent system for effectual prediction of pest population dynamics of Thrips Tabaci Linde (Thrips) on cotton (Gossypium Arboreum) crop is presented. The raw data used in the proposed system was obtained from the College of Agriculture, Raichur, India. Initially, the raw (pest surveillance) data is prepared by 1) Data preprocessing 2) Normalization and 3) Data transformation. The feed forward Multi-Layer Perceptron (MLP) Neural Network with backpropagation training algorithm is employed in the design of the intelligent system. The neural network is trained and tested with the data prepared. The experimental results portray the effectiveness of the proposed system in predicting pest population dynamics of Thrips on cotton crop. Moreover, a comparative analysis is performed between the proposed system and two of the existing works. The results showed that the proposed system based on feed forward neural networks was best suited for effective pest prediction.

show abstract

Neural Networks in Cultivation

Cited by 3 publications

References 2 publications

An intelligent system for predicting Thrips tabaci Linde pest population dynamics allied to cotton crop

An intelligent system for predicting Thrips tabaci Linde pest population dynamics allied to cotton crop

Using Neuromodels for Evaluating and Determining Productivity Technical Processes

A Prediction Model for Population Dynamics of Cotton Pest (Thrips tabaci Linde) using Multilayer-Perceptron Neural Network

Contact Info

Product

Resources

About