Abstract
Pests are very much harmful for crops and their number is increasing day by day. In order to control them several pesticides have been developed over the years. But, to apply pesticides, identification of their category and utility is highly essential. As a part of this process, we carry our study to compare the different methods of managing the pests of tomato in a field environment. Consequently, a CNN model is developed via mobile application for the identification of pests on tomato plants. The image pre-processing consisted of data cleaning and image augmentation. A training accuracy of 0.9985 with a test accuracy of 0.9891 is attained. It provides insight to farmers on this said application of CNN, based on the best pest management techniques, derived via comparison.
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Dharmasastha, K.N.S., Banu, K.S., Kalaichevlan, G., Lincy, B., Tripathy, B.K. (2022). Classification of Pest in Tomato Plants Using CNN. In: Mohanty, M.N., Das, S., Ray, M., Patra, B. (eds) Meta Heuristic Techniques in Software Engineering and Its Applications. METASOFT 2022. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-11713-8_6
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DOI: https://doi.org/10.1007/978-3-031-11713-8_6
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