A Comparison of Machine Learning Methods on Hyperspectral Plant Disease Assessments

As plant diseases could cause agricultural production and economic loses, there is a need of fast and effective plant disease detection and assessment methods. Non-destructive methods have gained popularity among these methods as they do not affect plant growth while examining plant health conditions. Not only plant diseases can be detected but also production can be improved with proper quality controls. Hyperspectral imaging is one of the non-destructive examination techniques which have been widely applied in agriculture. Hyperspectral image analysis has been applied to different problems including plant disease detection and assessments. It provides not only spatial image but also spectral information of the observed object. This research has aimed to compare two hyperspectral image analysis methods: stepwise discriminant analysis (SDA) and spectral angle mapper (SAM) and the proposed Simple Slope Measure (SSM) method in strawberry foliage Anthracnose assessment. Anthracnose is one of the most devastating diseases for strawberries. Anthracnose disease can affect the whole plant and may result in 100 percent fruit loss from crown and fruit rot. Hence, an early detection of the Anthracnose disease will be beneficial to ensure production and quality of strawberries. This research has shown that the three different Anthracnose infection status (healthy, incubation and symptomatic) could be separated by the methods examined. The performance of these disease assessment models were evaluated and compared. The examination outcomes prove the feasibility to assess strawberry foliage Anthracnose nondestructively and as early as the symptoms not visible to naked eyes. As soon as early detections of the Anthracnose disease are achievable in the strawberry field, the damage to strawberry production due to the spread of Anthracnose disease could be reduced.