Hello everyone,

I am a junior engineer who was recently hired by a smart agriculture startup specializing in hydroponic and aquaponic greenhouses. They want to integrate computer vision models for detecting pests and diseases in plants. My job is to develop these vision models, but I am facing some challenges:

Problem:

1. Lack of Data: The company does not have enough images and the images that are available do not seem appropriate for the task.
2. Poor image quality: The images provided have a very low resolution (plant segments are only about 40×40 pixels).

My proposed solution: a two-phase cascade network:

1. Anomaly detection network: To identify areas of potential problems (healthy vs. unhealthy areas), ideally by producing a segmentation map.
2. Classification Network: To analyze close-up images of problem areas and classify specific diseases.

Possible problems:

1. Image Resolution: The current image quality appears too low for accurate results.
2. Plant growth cycles: The anomaly detection network must account for normal size differences as plants grow to avoid false positives.

I have included sample images to illustrate the data quality issue.

I would greatly appreciate any advice or suggestions you could provide to help this junior engineer overcome this challenge. Thanks in advance for your help!

sample 1 – crop 1

example 1

example 2

sample 1 – crop 2