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Identifying region specifc seasonal crop for leaf borne diseases by utilizing deep learning techniques

Jayagopal Prabhu, Rajendran Sukumar, Mathivanan Sandeep Kumar, Sathish Kumar Sree Dharinya, Raja Kiruba Thangam, Paneerselvam Surekha

Acta Geophysica

2022

Vol. 70, no 6

2841--2854

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.