Plant Pathology Treatment System using Machine Learning

Registration ID: IJNRD_193051

Published ID: IJNRD2304640

DOI: Click Here to Get

Prajual Premanand Kotian , Aniket Ravindra Patil , Akash Vinod Harad , Priyanka Kamble

CNN, Disease Treatment, Fertilizer Recommendation, Image Classification, KNN, Plant Pathology Treatment, Random Forest, SVM

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The recent advances in computer vision made possible by deep learning have paved the way for computer-assisted disease diagnosis. Using a public dataset of approximately 20000 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 3 crop species and 15 diseases. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward computer-assisted crop disease diagnosis on a massive global scale.

Published Paper   E-Certificate

Prajual Premanand Kotian, Aniket Ravindra Patil, Akash Vinod Harad, & Priyanka Kamble (April-2023). Plant Pathology Treatment System using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), g312-g318. https://ijnrd.org/papers/IJNRD2304640.pdf

Volume 8 Issue 4, April-2023

Pages : g312-g318

Paper Reg. ID: IJNRD_193051

Published Paper Id: IJNRD2304640

Downloads: 000121979

Research Area: Information Technology 

Country: Palghar, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304640.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304640

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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