Citation:

Supriya Bhosale, Aditi Chhabria, 2023. "Survey on Disease Identification and Severity Level Estimation on Plant" ESP Journal of Engineering & Technology Advancements  3(1): 87-91.

Abstract:

The most nutritive crop that is being cultivated across the globe is the tomato plant. Moreover, it has a vital impact on the growth of the agricultural economy in terms of cultivation and export levels Plants not only contains protein, but also has pharmacological properties that safeguard the people from conditions like “high blood pressure, hepatitis, gingival bleeding”, etc. Nowadays, they are utilized in a large- scale, and as a consequence of this, the market for plants is also getting increased. Statistics reveal that the small producers produce more than 80 percent of plant and therefore, the economic losses are more than 50 percent due to the insects and pathogens. The primary issues affecting the plants development are pathogens and insect pests, so researching the detection of crop diseases is especially important. The management of plants diseases is indeed a difficult process that requires constant care during the growing season and is responsible for the substantial fraction of overall production level. Earlier identification could significantly minimize the treatment costs, mitigate the severity of chemical contaminants, and alleviate the chances of yield loss. Present methods of disease diagnosis are restricted in terms of time required for qualified technicians to physically identify and evaluate the pathogens, exacerbated by the number of plants in commercial greenhouses and the small scale of indications at the early stage of disease. Usually, the cost and complexity involved in disease detection restricts the outbreak exploration to an occasional cycle or limited sampling. Molecular processing, spectroscopy, and examination of volatile organic compounds have been used in the studies of the automatic detection processes. Though, they are costly and inefficient to implement on a real - time operating scale.

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Keywords:

Automatic Detection Processes. Disease Detection, Real - Time Operating Scale.