Classification of Diseased Leaves in Plants Using Convolutional Neural Networks

Title

Classification of Diseased Leaves in Plants Using Convolutional Neural Networks

Creator

Reddy B.D.K.; Iyer L.S.

Description

The article focuses on the classification of diseased leaves using a machine learning algorithm. The main focus in agriculture is controlling pests and weeds, for which farmers spray chemical pesticides to get a good yield. The issue here is over-usage and under-usage of pesticides, which might harm the end consumer. To achieve the goal of reducing pesticide use and detecting pests in the crop early, the machine learning algorithm is deployed on the leaf image. The image data of the leaf of the cauliflower plant is collected for 40days. The data was collected from the day the plant was seeded in a pot until the day it was ready to be planted in the soil. From this data, the pest attack on the plants is tracked without the application of pesticides. To achieve this, the CNN algorithm is used on the collected image data. The outcome of the study would be to classify the diseased leaves based on the pest attack and know the right time to spray the pesticides to reduce the damage to the plant. This also reduces the use of pesticides and costs to the farmer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 139-149.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Convolutional neural network; Diseased leaf

Coverage

Reddy B.D.K., School of Business and Management, Christ University, Bangalore, India; Iyer L.S., School of Business and Management, Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981970974-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-0975-5_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197267405&doi=10.1007%2f978-981-97-0975-5_12&partnerID=40&md5=2de148875332e07e14e95707beb2f4a8