| Type of Material: | Thesis |
| Title: | Design and development of a model to classify crop foliar diseases |
| Researcher: | Naik, Akruti N |
| Guide: | Thaker, Hetal R |
| Department: | Computer Science |
| Publisher: | Atmiya University |
| Place: | Rajkot |
| Year: | 2022 |
| Language: | English |
| Subject: | Decision Tree | Engineering and Technology | KNN | Logistic Regression | Moong Leaf | SVM | Computer Science | Computer Science and Image Processing | Computer Science Artificial Intelligence | Crop Disease | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Computer Science, Atmiya University, Rajkot; 2022 |
| Fulltext: | Shodhganga |
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| 005 | 2026-10-08 17:26:14 | |
| 008 | __ | 261008t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464035 |
| 040 | __ | |dIN-AhILN|aATMI_360005 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aNaik, Akruti N |
| 110 | __ | |aComputer Science|0U-0967|dRajkot|bAtmiya University|ein |
| 245 | __ | |aDesign and development of a model to classify crop foliar diseases |
| 260 | __ | |aRajkot|c2022|bAtmiya University |
| 300 | __ | |a245|dDVD|cA4 Size |
| 502 | __ | |bPhD|d2022|cComputer Science, Atmiya University, Rajkot |
| 518 | __ | |oDate of Award|d2022 |
| 518 | __ | |d2019|oDate of Registration |
| 518 | __ | |d2022|oDate of Completion |
| 520 | __ | |aa controlled environment is a data item (image) that comprises only a single subject (leaf) and a white background. In an uncontrolled environment, an image contains the Mung leaf, background noise like stems, ground, other Mung leaves, etc. In combined environment images of both the controlled and uncontrolled environments are merged together. Seven different classifiers namely Support Vector Machine (SVM), KNN (K Nearest Neighbor), AdaBoost (Adaptive Boosting), GaussianNB (Gaussian Naive Bayes), DTC (Decision Tree Classifier), LogisticRegression and Custom CNNs with different architectures have been trained and compared to each other. Researcher aims at detecting 3 mung leaf disease categories and a healthy leaf category. The model extracts complex features of various diseases. Early detection will help farmers to improve their productivity. The main objective was to automate Mung Leaf disease identification using advanced machine learning and deep learning approaches and image data. Among all the classif |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aDecision Tree |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aKNN |
| 653 | __ | |aLogistic Regression |
| 653 | __ | |aMoong Leaf |
| 653 | __ | |aSVM |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science and Image Processing |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aCrop Disease |
| 700 | __ | |aThaker, Hetal R|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/449354|yShodhganga |
| 905 | __ | |afromsg |
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