Estimation of Brown Plant Hopper Level in Rice Crop using Artificial Neural Network

Early prediction of any plant disease helps to reduce the loss in yield due to the disease. In recent times, brown plant hopper outbreaks have increased all over India, which have caused heavy loss to the rice production. In this paper, we speculate the various environmental factors and their effect on presence of brown plant hopper (Nilaparvatalugens Stål) in relation with the yield of rice crop. Relationship of weather parameter were explored with peaks of brown plant hopper (BPH) outbreak data, which was available of Ludhiana (Punjab) during 2007- 2011 on government of India official agriculture data website. The purpose of this research is to highlight the importance of the planning strategies for agriculture with abrupt change in weather conditions. Which helps to protect crops and keep their productivity high. Proposed prediction model works on neural network in which input are weather parameters that effect the presence of brown plant hopper. Model estimate the probable level of brown plant hopper, which effect the productivity of rice.