Pests Detection Using Artificial Neural Network and Image Processing: A Review

The Indian economy relies heavily on the cultivation of rice. As the global population grows, so does the need for rice farming. To maximise rice crop growth, early detection of pests is critical. It's still a challenge for farmers in our country to preserve their crops from external hazards, such as bug infestations in agricultural areas. Increasing agricultural output over the long term necessitates rapid and precise identification of plant diseases. Plant anomalies, such as disease, pests, nutritional inadequacies, or harsh weather, have traditionally been diagnosed by human experts. However, this can be costly, time demanding, and even impracticable in some situations. Insect pests can have a negative impact on the country's agricultural output. In most cases, farmers and other professionals keep a close eye on the plants in order to look for disease. This, however, is a labor-intensive, costly, and imprecise approach. The use of image processing techniques for automatic detection yields quick and accurate results. The productivity and quality of plants are strongly influenced by diseases and pests that affect the plants. Digital image processing can be used to identify plant diseases and pests. Recent advances in digital image processing have been enabled by deep learning, which has outperformed more traditional approaches in this paper we are reviewing some image processing technique that's are done by different authors. In this paper mainly we are focus on the image processing technique with the help of artificial neural network for automatic pest detection. And here we are also define basic methodology that can be used for pest detection by using image processing and artificial neural network.