Plant diseases are causing a lot of damage within the agriculture industry. Experts have estimated that because of diseases, around 30% of food crops are lost. Besides the lower quantities of produced crops, diseases also lower their quality. In order to stop the spread of diseases among the crops, it is important to discover them early. Today we can use technology to assist us. Deep learning models for detecting said diseases are showing good results, but they often require big amounts of images for training, which we often do not have. In our master's thesis we developed a system that requires relatively low amounts of data for training. Our algorithm first locates and segments a certain leaf or fruit. After that it divides it into smaller pieces and extracts key information from them, and based on that it later classifies the plant into a certain class (for example, »healthy« or »unhealthy«). Experiments with images of guava, wheat, and coffee showed that our approach, when using only 1 to 10 images, classifies images better than the comparing models.
|
