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Title: :  Machine Vision for Plant Malfunction Identification
PaperId: :  22729
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22729
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mande Srinivasa RaoVasireddy Venkatadri Institute of Technology
Pinninti LokeshVasireddy Venkatadri Institute of Technology
Siddaboina BalajiVasireddy Venkatadri Institute of Technology
Tummeti Sujith Venkata Naga SaiVasireddy Venkatadri Institute of Technology
Uppalapati Mariya BabuVasireddy Venkatadri Institute of Technology

Abstract

Machine Learning
Plant Malfunction Identification, Convolution neural networks (CNNs), Machine Learning, data collection, data preprocessing.
The Machine Vision for Plant Malfunction Identification is aimed at using machine learning, particularly convolutional neural networks (CNNs), to solve the challenge of identifying plant diseases at an early stage. The project follows a structured approach, involving data collection, preprocessing, model development, training, and performance assessment. By utilizing a carefully curated dataset containing images of both healthy and diseased plants, the CNN is trained to recognize specific patterns and features associated with various plant ailments. Through this training process, the neural network becomes adept at distinguishing between healthy and diseased plants by detecting subtle visual cues indicative of different diseases. This methodology holds great promise for improving the early detection of plant diseases, potentially reducing crop losses and enhancing agricultural productivity. Furthermore, the application of machine learning in agriculture underscores the potential for technology-driven solutions to address pressing challenges in the agricultural sector. By automating disease detection processes, farmers and agricultural stakeholders can benefit from timely intervention strategies, leading to more efficient crop management practices and increased food security. Additionally, the scalability and adaptability of machine learning algorithms offer opportunities for continual improvement and refinement of disease detection systems. Overall, the integration of machine learning and CNNs in plant disease detection represents a significant advancement in agricultural technology, with far-reaching implications for global food production and agricultural sustainability.

Citations

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IJARIIE Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. "Machine Vision for Plant Malfunction Identification" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 219-223
MLA Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. "Machine Vision for Plant Malfunction Identification." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 219-223.
APA Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, & Uppalapati Mariya Babu. (2024). Machine Vision for Plant Malfunction Identification. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 219-223.
Chicago Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. "Machine Vision for Plant Malfunction Identification." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 219-223.
Oxford Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. 'Machine Vision for Plant Malfunction Identification', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 219-223. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Machine_Vision_for_Plant_Malfunction_Identification_ijariie22729.pdf (Accessed : ).
Harvard Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. (2024) 'Machine Vision for Plant Malfunction Identification', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 219-223IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Machine_Vision_for_Plant_Malfunction_Identification_ijariie22729.pdf (Accessed : )
IEEE Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu, "Machine Vision for Plant Malfunction Identification," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 219-223, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Machine_Vision_for_Plant_Malfunction_Identification_ijariie22729.pdf [Accessed : ].
Turabian Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. "Machine Vision for Plant Malfunction Identification." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver Mande Srinivasa Rao, Pinninti Lokesh, Siddaboina Balaji, Tummeti Sujith Venkata Naga Sai, and Uppalapati Mariya Babu. Machine Vision for Plant Malfunction Identification. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 219-223. Available from: https://ijariie.com/AdminUploadPdf/Machine_Vision_for_Plant_Malfunction_Identification_ijariie22729.pdf
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