Detecting infection of plant diseases by classifying plant photos

One or more non-transitory computer-readable media storing one or more sequences of instructions which when executed cause one or more processors to execute a method of configuring and utilizing a convolutional neural network for plant disease recognition, the method comprising:

• receiving a set of photos of plants infected with a plurality of diseases,
• the set of photos showing leaves with a plurality of marked regions having multiple aspect ratios, each marked region being associated with a label of a disease of the plurality of diseases and showing at least one lesion caused by the disease,
• a specific photo of the set of photos showing a specific leaf having a specific marked region showing multiple lesions,
• a total size of the multiple lesions being greater than a predefined percentage of a size of the specific marked region;
• determining a group of anchor boxes from the plurality of marked regions for each of a series of convolutional layers of a single shot multibox detector (SSD),
• the SSD configured to receive an image and assign each of one or more areas of the image into at least one of a plurality of classes corresponding to the plurality of diseases,
• the group of anchor boxes having distinct aspect ratios and corresponding to various features of the plurality of classes;
• mapping each of the plurality of marked regions to one of the groups of anchor boxes;
• building the SSD from the group of anchor boxes, the set of photos having the plurality of marked regions, the associated plurality of labels, and the associated plurality of mappings;
• receiving a new image from a client device;
• applying the SSD to the new image to identify symptoms of one or more diseases in one or more areas of the new image;
• transmitting data related to the one or more diseases or one or more areas of the new image to the client device.