CROP DISEASE DETECTION SYSTEM

Abstract

ABSTRACT A crop disease detection system (100) comprising a data acquisition module (102) configured to capture images of plant leaves using a digital imaging device, a processing module (104) configured to resize, normalize, and augment the captured images to enhance image quality for analysis, a classification module (106) configured to process the pre-processed images using a convolutional neural network (CNN) to detect and classify diseases based on learned disease patterns; and a user interface (108) configured to display the classification results, including disease identification and a confidence score, to end-users. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to agriculture technology, specifically to systems and methods for detecting crop diseases using image processing and machine learning techniques.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Crop diseases are a significant challenge in agriculture, impacting crop yield and quality, which threatens food security. Traditional disease detection methods, like visual inspections, are time-consuming and often inaccurate due to the complexity and sim , Claims:WE CLAIM:
1. A crop disease detection system (100), comprising:
a data acquisition module (102) configured to capture images of plant leaves using a digital imaging device;
a processing module (104) configured to resize, normalize, and augment the captured images to enhance image quality for analysis;
a classification module (106) configured to process the pre-processed images using a convolutional neural network (CNN) to detect and classify diseases based on learned disease patterns; and
a user interface (108) configured to display the classification results, including disease identification and a confidence score, to end-users.

2. The system (100) as claimed in claim 1, wherein the classification module is further configured to apply data augmentation techniques, including rotation, flipping, and zooming, to enhance the CNN's robustness to varying image conditions and improve accuracy in disease detection.

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