USING DEEP CONVOLUTIONAL NEURALNETWORK FORPLANT LEAF DISEASEDETECTION

• Published in: NeuroQuantology Vol. 20; no. 8; p. 8624
• Main Authors: Jolly Masih, Rajesekaran, Rajkumar, Bhagwat, Abhijit, Rokade, Kapil, SenthamilSelvan, R
• Format: Journal Article
• Language: English
• Published: Bornova Izmir NeuroQuantology 01.01.2022
• Subjects: Algorithms; Artificial neural networks; Classification; Developing countries; Economic development; Insects; LDCs; Machine learning; Mangoes; Pests; Plant diseases; India
• ISSN: 1303-5150
• DOI: 10.14704/nq.2022.20.8.NQ44884

Agriculture plays an important role in economic growth of any country. The poor and inappropriate plant disease identification techniques might lead to heavy crop losses impacting national nutrition and health security. Most of the farmers from developing countries such as India, use laborious and time taking traditional methods for early disease identification. Use of machine learning techniques such as Convolutional Neural Network (CNN) are helpful in early-stage leaf disease detection and classifications. Such techniques can be easily used by Indian farmers to save their precious crop from harmful insect, pests and disease. Machine learning (ML), plays an important role in identification and cure of common and rare plant diseases. Use of machine learning techniques is helpful in early-stage leaf disease detection and classifications. ML based plant disease identification algorithms enable farmers to identify plant diseases on time and to provide proper cure against the diseases. India is among the largest producer of fresh mangoes. The mango called Alphonso is one of world’s most popular fruit. This study investigates the role of ML and CNN inidentification of various Mango plant leaf diseases in India and discusses its benefits to the Indian farmers.