Leveraging MobileNet & InceptionNet for Improved Crop Disease Prediction
Keywords:
MobileNet, InceptionNet, Random Forest ClassifierAbstract
Agricultural production and food security are greatly impacted by the ability to predict crop diseases. Recent years have witnessed encouraging outcomes in the automation of disease detection processes. This study investigates the effectiveness of leveraging MobileNet and InceptionNet as feature extractors for enhancing crop disease prediction. We propose a novel approach that utilizes transfer learning to leverage the pre-trained weights of MobileNet and InceptionNet architectures, fine-tuning them on a dataset of crop disease images. The extracted features are then fed into a classification model for disease prediction. The results of the research show that our proposed method compared to traditional feature extraction techniques. The combination of MobileNet and InceptionNet substantially improves precision, responsiveness, and discrimination of crop disease prediction, thereby providing a robust and efficient solution for early disease detection in agriculture. Experimental result of MobileNet Random Forest Classifier (RFC) model achieved the highest accuracy of 92.3%. This study contributes to propelling precision agriculture forward by laying the groundwork for automated crop disease diagnosis, ultimately aiding farmers in making timely and informed decisions to mitigate crop losses.
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