The Science behindTo automate some of the expert processes we leverage some of the latest computer vision and machine learning techniques and apply them to three specific tasks in this survey.
- The objective here is to assess the feasibility of automated computer vision based diagnosis of CMD. Feature extraction techniques based on color and shape are used to extract features from the images of the healthy and CMD-infected cassava plants and classification using a set of standard classification methods (naive Bayes, two-layer MLP networks, support vector machines, k-nearest neighbor and divergence- based learning vector quantization) is applied to determine the diagnosis of the plant(image). This diagnosis is either done at the server end or on the phone.
Necrotized roots infected with CBSD
1. Automated diagnosis
|Depiction of survey process - (a) Image capture, (b) Automated diagnosis, (c) Real-time mapping|
2. Automated whitefly count
|Whitefly detection and counting|