Tomato Plant Leaves Disease Classification Using KNN and PNN

Tomato Plant Leaves Disease Classification Using KNN and PNN

Balakrishna K. (Maharaja Institute of Technology, Mysore, India) and Mahesh Rao (Maharaja Institute of Technology, Mysore, India)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJCVIP.2019010104


Plant diseases are a major threat to the productivity of crops, which affects food security and reduces the profit of farmers. Identifying the diseases in plants is the key to avoiding losses by proper feeding measures to cure the diseases early and avoiding the reduction in productivity/profit. In this article, the authors proposed two methods for identification and classification of healthy and unhealthy tomato leaves. In the first stage, the tomato leaf is classified as healthy or unhealthy using the KNN approach. Later, in the second stage, they classify the unhealthy tomato leaf using PNN and the KNN approach. The features are like GLCM, Gabor, and color are used for classification purposes. Experimentation is conducted on the authors own dataset of 600 healthy and unhealthy leaves. The experimentation reveals that the fusion approach with PNN classifier outperforms than other methods.
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1. Introduction

Agriculture is the backbone of Indian economy with around 70% of the population earning their livelihood directly or indirectly from this sector as per the 2011 census (Government of India, 2017). Today India ranks second worldwide in farm output. The economic contribution of agriculture to India's GDP (Gross Domestic Product) is steadily declining with the country's broad-based economic growth. Still, agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India (Government of India, 2017). Agriculture is a critical sector as it plays major part in the development of the Indian economy and food security for the vast population of the country. As India is a rain fed area, agriculture majorly depends on the climatic conditions, and due to the adverse effect of nature like excessive or deficit rainfall, extremely hot or dry weather which affects the growth stages of a plant, which will impact on the crop productivity. Current agriculture modernization practice has opened a new avenue due to the globalization and liberalization policies of this country (Joshi, 2015). Even though many farmers try to follow the modern agricultural practice they fail to achieve higher productivity due to various reasons. A special attention for development planning in the field of agriculture is needed which will eventually help the sector to sustain, hence a sustainable agriculture would be the choice.

Tomato is an important widely distributed fruit or vegetable crop due to its higher consumption rate. India is the second largest producer of tomato next to China (Government of India, 2017). Tomato being a short duration crop of 2 to 4 months, farmer select the best seeds to sow in the field and monitor the plant regularly creating a suitable environment required for the plants. During the growth period of this plant it is attacked by a number of diseases from bacteria, fungal and virus. Bacteria grow by decaying the organic matter in soil and multiply themselves in the tissues of the plant and Fungi looks like thread vegetative growth, in the presence of moisture they germinate spores and produce infection as both of these are microorganisms. Virus diseases cause a streaking of the stem, which will kill the growing conditions that affect the growth of the plants (Mahlein, 2016). Some of the major diseases affecting the growth of the tomato plants are Verticilium wilt, Powdery mildew, Leaf miners, Septoria leaf spot and Spidermites. From all these diseases lead to a reduction in productivity and downward in the profit to the farmer (Joshi, 2015).

Typically, detection of diseases in the plant leaves has been carried with the experience by farmer’s i.e. visual inspection of the cultivators and they apply some fertilizers or pesticides to overcome from that disease. The potential of detecting different diseases in the plant is desirable, where in the growth stages of plants may be affected simultaneously by many pathogens, such as bacteria, fungal and viruses (Mahlein, 2016). Nowadays, automatic detection of diseases attracts researchers from the different domains due to its benefit in monitoring large field and the consistency which it can provide (Gavhale and Gawande, 2016). A modern approach for detecting and analysis of leaf diseases are lacking to reach the expectation level. Even though using various detection, extraction and classification techniques in image processing are available but they have not been applied and proven on some of these vegetable plants under various conditions. Further farmer needs a better detection model to identify the different diseases affecting from the pathogens and measures to cure that disease so the application of pesticides, etc., can be in precise quantity.

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