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Published Paper Details:
AUTOMATED CROP DISEASE AND PESTICIDE RECOMMENDATION SYSTEM USING RESNET 50
Authors
Kothakota Raghavendra,Lokireddy Arunkumar Reddy,Magham Pavan Sai,Oppicharla Prasadu
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Keywords
Crop Disease Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Precision Agriculture, VGG16, ResNet50, Smart Farming, Plant Health Monitoring, Transfer Learning.
Abstract
Crop diseases significantly impact agricultural
productivity and food security worldwide. Early and accurate
identification of crop diseases is crucial for effective
management and prevention of yield loss. Traditional
methods of disease detection rely heavily on expert
knowledge and manual inspections, which are timeconsuming, subjective, and often inaccessible in remote
areas. To overcome these limitations, this study explores the
use of deep learning models for the automatic identification
of crop diseases.
By leveraging convolutional neural networks (CNNs), a
widely used deep learning architecture, we trained a model
on a publicly available dataset of infected and healthy crop
leaf images. The system was designed to classify multiple
types of diseases across various crop species with high
accuracy. The model was trained and validated using
techniques like data augmentation, transfer learning, and
fine-tuning of pre-trained networks (e.g., VGG16, ResNet50)
to improve performance and generalisation.
The proposed deep learning-based approach offers a
scalable, efficient, and low-cost solution for crop disease
detection that can be integrated into mobile applications or
deployed on IoT devices for real-time monitoring. This
system holds great potential for empowering farmers with
actionable insights, reducing dependence on expert
intervention, and improving overall crop health
management practices.
IJCRT's Publication Details
Unique Identification Number - IJCRT2605270
Paper ID - 305357
Author type - Indian Author
Page Number(s) - c257-c261
Pubished in - Volume 14 | Issue 5 | May 2026
DOI (Digital Object Identifier) -
No Of Downloads - 49
Author Country - India, 522647, vinukonda, vinukonda, 522647, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Kothakota Raghavendra,Lokireddy Arunkumar Reddy,Magham Pavan Sai,Oppicharla Prasadu, "AUTOMATED CROP DISEASE AND PESTICIDE RECOMMENDATION SYSTEM USING RESNET 50", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 5, pp.c257-c261, May 2026, Available at :http://www.ijcrt.org/papers/IJCRT2605270.pdf
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