Potato Leaf (Healthy and Late Blight)

Many researchers used traditional machine learning algorithms which require a handcrafted feature extraction to detect a given image. Besides, the data collected were under a laboratory setup which makes it less reliable while testing in real cultivation farms captured images of the potato. Heterogeneous image datasets were collected from (Holeta, Ethiopia) potato farm with the help of two plant pathologists.

The dataset is correctly labeled with two classes as ‘Healthy’ and ‘Late Blight’, and the image has a variety meaning some of the images are captured with less noisy background images and others with a highly noisy environment. Under the ‘Late Blight’ class 63 images were collected and under the ‘Healthy’ class 363 images were collected. Finally, the prepared dataset could be used for different research that aimed at plant disease detection and classification.