It is very important to know the disease of your crops since the very beginning. Discover your plant disease using a photo of your plant leaf. Leaves are the most commonly observed part for detecting an infection.
Take note that symptoms in some plants can appear slowly, so usually it is difficult for farmers to notice and correctly diagnose the plant disease.
+ This app could diagnose the disease of the following diseases:
Apple scab, Cedar apple rust, Black rot, Powdery mildew, Cercospora leaf spot, Gray leaf spot, Common rust, Northern leaf blight, Eriophyes vitis, Grape esca (Black Measles), Leaf blight (Isariopsis leaf spot), Orange haunglongbing (Citrus greening), Leaf scorch, Early blight, Leaf Mold,
Septoria leaf spot, Two-spotted spider mite, Tomato target spot, Mosaic virus, Tomato yellow leaf curl virus, Bacterial spot and Late blight.
+ So you can take a picture of the following plant leaves to detect them:
Tomatoes, Pepper, Apple, Pear, Quince, Grape vines, Cabbage, Broccoli, Potato, Lemon, Orange, Sweet potato, Cauliflower, Raspberry, Squash, Corn, Peach, Strawberry, Roses, Apricot, Nectarine and Plum.
The rapid, accurate diagnosis of disease severity will help to reduce yield losses.
+ You can have more reliable source of food
+ You can grow more crops.
+ You can protect them.
This app allows the opportunity to use computer vision techniques to monitor the disease type and severity and increase yields.
A huge image database (thousand of images) has been used to get the maximum accuracy using artificial neural networks .
This app has also an option to report an image that couldn’t be recognised. If that is the case, it is automated that you can report it immediately by email to the author. So it guarantees that the database image will be improved constantly.
The intended purpose of this app is help the farmers to build a better and bigger crops, which results in a more sustainable world.
Diseases are a major thread to losses of modern agricultural production. Keep your crops healthy.
For any doubt, please contact me at email@example.com.
Improved the accuracy of the disease identifier, as more photos has been added to the dataset. So the machine learning engine has become more adaptive.