Fast Parallelizable Cassava Plant Disease Detection using Ensemble Learning with Fine Tuned AmoebaNet and ResNeXt-101
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Abstract
The study focuses on applying advanced machine learning approaches, namely deep learning models and ensemble learning, to develop an automated system for Cassava Brown Streak Disease (CBSD) detection using robotic systems. We leverage an extensively used dataset to evaluate our proposed methodologies. The dataset comprises thousands of labeled images illustrating various stages of CBSD infection. Our experiment is furnished with high-resolution cameras and sophisticated image-processing algorithms. We finetune AmoebaNet and ResNeXt-101 32x16d models using the dataset to distinguish between healthy and diseased cassava plants. Ensemble learning is then applied to consolidate the prediction outputs from both models, consequently enhancing overall diagnostic accuracy. The implemented system showcases exceptional performance, delivering high precision and recall rates in recognizing CBSD cases. Through automation, our solution significantly diminishes the reliance on human expertise, streamlines the diagnostic procedure, and extends the reach of CBSD detection applications. This pioneering research marks a significant stride forward in fortifying food security and promoting sustainable agriculture in regions affected by CBSD. Further details about the dataset usage will be discussed in the Methodology section.
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