Abstract
A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks
Highlights
The agricultural industry plays an important role in the economy
Faster RCNN inception v2 performs better if accuracy is a priority. These two models can be used depending on the purpose of the detection of the Cassava Phytoplasma Disease (CPD)
The most important factor to be considered must be its accuracy since CPD detection is the main objective of this study
Summary
The agricultural industry plays an important role in the economy. Plant illness is caused by climatic circumstances, exacerbated by the exponential trend of population growth. The major challenges of sustainable development include reducing pesticide use, the expense of preserving the environment, and the cost of building quality. Exact, and timely decisions may reduce pesticide use [1]. Innovation is commonly used for plant disease prediction. The concept of image processing combined with information mining improvements aids in identifying plant diseases. As an important aspect of image processing [2][3][4], object detection has become one of the popular international research fields. The powerful ability with feature learning and transfer learning of Convolutional Neural Network (CNN) has received growing interest within the computer vision community, making a series of important breakthroughs in object detection [5][6]. It is a significant application to apply CNN to object detection for better performance
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