Details

Autor(en) / Beteiligte

• Jyothi, E. V. N.
• Ravindra Babu, Ch
• Sadineni, Giribabu
• Annapurna, D.

Titel

Plant Leaf Disease Detection Using Auto Encoder And Cnn

Ist Teil von

• 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), 2023, p.1-6

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Plants are regarded as indispensable due to the fact that they constitute the primary source of energy generation for humanity as a result of the nutritive, therapeutic, and other benefits that they possess. Plant diseases can harm the leaf at any moment between crop farming, which can result in massive damages to crop yield and economic market worth. Thus, the diagnosis of leaf diseases is an extremely important part of the agricultural industry. However, it requires a significant amount of manual labour, more time for preparation, and in- depth understanding of plant pathogens. In many instances, these researchers also obtained significant results in both scenarios. This previous research served as inspiration for the model of plant leaf categorization that we suggested in this paper, which makes use of auto encoders for feature extraction and is pre-trained with MobileNetV2.Our experimental results demonstrate that our proposed model outperforms state-of-the-art models.