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- Title
Identifying region specific seasonal crop for leaf borne diseases by utilizing deep learning techniques.
- Authors
Jayagopal, Prabhu; Rajendran, Sukumar; Mathivanan, Sandeep Kumar; Sathish Kumar, Sree Dharinya; Raja, Kiruba Thangam; Paneerselvam, Surekha
- Abstract
India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season's growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes.
- Subjects
INDIA; CLIMATE change; IMAGE recognition (Computer vision); SEASONS; CROPS; FOOD crops; COMPUTER vision; BLIGHT diseases (Botany); DEEP learning; TOMATO yellow leaf curl virus
- Publication
Acta Geophysica, 2022, Vol 70, Issue 6, p2841
- ISSN
1895-6572
- Publication type
Article
- DOI
10.1007/s11600-022-00759-x