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

Abstract

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.

A Study on Developing Analytical Model for Groundnut Pest Management Using Data Mining Techniques

“…Jyothi Patel et.al [4] made an attempt to analyze the thrips dynamics in the cotton crop using Multi-layer perceptron Neural Network technique. P.Kamalak Kannan et.al [11] describes that efficient yield of crop and concern about organic farming can be obtained using various analysis and prediction methods of Data Mining.…”

K-means Based Automatic Pests Detection and Classification for Pesticides Spraying

“…If pest detection is not done, it will proceeds towards the reduction of crops,which results in increase of poverty. It is required to use a proper well organized pest detection methodology which may help us to reduce these pesticides and increase a healthy yields [3]. We have seen a typical thing that the vast majority of those models were proposed for some particular condition.…”

“…In [3], author presented a crop based detection technique. Authors has developed a cotton crop pest's dataset.…”

Dynamics and pest and natural enemies dispersion in cowpea and colored cotton in sole or intercropping systems

“…Several models have been developed to study pest dynamics in sole crops or intercropping systems (Matis et al 2005;Patil & Mytri 2013;Tonnang et al 2017). Integrodifferential equations (IDEs) and mechanistic models can be used as a powerful tool since they performed the insect dynamics in the same proportion as the classical nonlinear regression (Wang, Cao & Huang 2013).…”

“…On the other hand, the stochastic model can be useful to estimate the variability in the peak of aphid when cumulative counts are studied (Matis et al 2005). Intelligent systems for effectual prediction of pest population dynamics of sucking insects on cotton were important since it allowed the comparison of the fluctuation of pest over weeks in different crops, being the model applied to evaluate Thrips tabaci in cotton and the best to explain its dynamics (Patil & Mytri 2013).…”