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Title: :  MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK
PaperId: :  26027
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26027
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
BODIMALLA TEJA VARDHAN REDDYVasireddy Venkatadri Institute of Technology
VINEELA THONDURIVasireddy Venkatadri Institute of Technology
GUDAPATI CHITRAHAS BALAJIVasireddy Venkatadri Institute of Technology
GANNAMANENI SASANKVasireddy Venkatadri Institute of Technology
DESAVATH YASWANTH NAIKVasireddy Venkatadri Institute of Technology

Abstract

Electronics and Communication Engineering
Plant Disease Detection, Convolutional Neural Networks, Deep Learning, Image Classification, Data Collection, Data Preprocessing
The plant Disease Detection using CNN project is used to early and precise detection of plant diseases is vital for reducing crop loss and maintaining agricultural sustainability. This research suggests a deep learning-based method applying convolutional neural networks (CNNs) for automatic plant disease detection. The method adopts a systematic workflow consisting of data collection, preprocessing, model development, training, and assessment. A carefully selected dataset of images of healthy and diseased plants is used to train the CNN to identify characteristic patterns and features with respect to plant diseases. The model develops its ability to classify with an improvement that comes from recognizing even minute visual indications of various diseases. The new method enhances the effectiveness of early disease detection, which could reduce losses to agriculture and improve productivity. In addition, incorporating deep learning in precision agriculture highlights the role of technology-based solutions in alleviating critical agricultural challenges. Automating disease detection allows intervention strategies to be put in place promptly, enabling more efficient crop management practices and enhancing worldwide food security. The scalability and flexibility of deep learning algorithms also offer the potential for ongoing refinement and improvement of disease detection systems. In general, the use of CNNs in plant disease detection is a major breakthrough in agricultural technology that provides a robust, scalable, and efficient solution with far-reaching implications for sustainable agriculture and food production.

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IJARIIE BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. "MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 833-838
MLA BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. "MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 833-838.
APA BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, & DESAVATH YASWANTH NAIK. (2025). MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 833-838.
Chicago BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. "MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 833-838.
Oxford BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. 'MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 833-838. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/MOBILENETV2_FOR_PLANT_DISEASE_DETECTION___A_SCALABLE_DEEP_LEARNING_FRAMEWORK_ijariie26027.pdf (Accessed : ).
Harvard BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. (2025) 'MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 833-838IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/MOBILENETV2_FOR_PLANT_DISEASE_DETECTION___A_SCALABLE_DEEP_LEARNING_FRAMEWORK_ijariie26027.pdf (Accessed : )
IEEE BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK, "MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 833-838, Mar-App 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/MOBILENETV2_FOR_PLANT_DISEASE_DETECTION___A_SCALABLE_DEEP_LEARNING_FRAMEWORK_ijariie26027.pdf [Accessed : ].
Turabian BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. "MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver BODIMALLA TEJA VARDHAN REDDY, VINEELA THONDURI, GUDAPATI CHITRAHAS BALAJI, GANNAMANENI SASANK, and DESAVATH YASWANTH NAIK. MOBILENETV2 FOR PLANT DISEASE DETECTION : A SCALABLE DEEP LEARNING FRAMEWORK. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 833-838. Available from: http://ijariie.com/AdminUploadPdf/MOBILENETV2_FOR_PLANT_DISEASE_DETECTION___A_SCALABLE_DEEP_LEARNING_FRAMEWORK_ijariie26027.pdf
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