Pests Detection Using Artificial Neural Network and Image Processing: A Review | IEEE Conference Publication | IEEE Xplore

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


Abstract:

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 detec...Show More

Abstract:

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.
Date of Conference: 07-09 April 2022
Date Added to IEEE Xplore: 27 April 2022
ISBN Information:
Conference Location: Erode, India

I. Introduction

Economic growth and quality of life are both influenced by agricultural production, which serves as the backbone of the economy. All countries have an agriculture and food processing industry that plays an important role in increasing the quality of agricultural and food goods exported. Import earnings and domestic market demand drive the growth of food processing transformations in developing countries. It necessitates a lot of storage space, as well as regular maintenance of equipment and workplaces. Crop quality is negatively impacted by pest attacks, which are a major challenge in the agriculture sector [11] [12] [15]. Crop losses due to pests, diseases, and weeds are enormous, and the market for the final products is severely depressed as a result. Even the tiniest improvement in efficiency can make a huge difference in the profitability of a business. It has to deal with the insect attack on crops that affects the growth of the field crops. The massive amounts of production are largely due to the highly needed cash crops. There is a direct correlation between crop quality degradation and decreased agricultural yields due to the presence of insects. Insect losses must be monitored and evaluated to maintain the safety and quality of crops in agriculture. The threat of plant disease to the world's food supply has grown significantly. Each year, plant diseases cause global agricultural losses of 10–16 percent, costing an estimated $220 billion. There will be 9.1 billion people on the planet in 2050, according to a research from the Food and Agriculture Organization (FAO). In order to keep up with the rising demand for food, agricultural productivity must expand by up to 70 percent. There has been an increasing usage of pesticides such as nematicides to control plant diseases that have had negative impacts on the ecology [14] [16]. There is a critical need for robust early disease detection technologies to assure food security and the long-term survival of the agro-ecosystem. Fruits, vegetables, grains, legumes, and other crops all suffer when plant diseases destroy their quality and yield. Plants afflicted with lethal plant diseases are far more likely to perish than those that have not been affected. The cadang-cadang disease affects the coconut palm (Cocos nucifera L.). More than 40 million coconut palms have been destroyed by this illness since 1914, according to reports. A plant's growth is often slowed when its photosynthetic process is affected by illness [13]. According to research, fungi or fungal-like organisms are to blame for the majority of plant ailments. Non-bacterial, viral, and virological plant disease is more dangerous than neptide-induced illness.

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References

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