Identification and Detection System of Forage Health

Introduction

For farmers worldwide, alfalfa (Medicago sativa) is an important forage grass that contains a wide range of nutrients. However, like all crops, alfalfa is susceptible to infectious diseases, the occurrence of which can have a significant impact on alfalfa hay yield and quality, affecting the health of the alfalfa industry. There are more than ten kinds of alfalfa foliar diseases, some of which have similar symptoms, making it difficult to accurately diagnose and identify diseases by observing symptoms with the naked eye or pathogens with a microscope. Control of these diseases is an important part of economical alfalfa production. Alfalfa diseases can lead to reduced forage yield, reduced forage quality, and reduced stand persistence.

Solutions

Based on years of experience in forage research, Lifeasible is the ideal forage service partner. With the rapid development of computer and information technology, we provide fast, accurate, and automated diagnosis and identification of alfalfa foliar diseases using image processing technology. We develop customized processes for alfalfa disease diagnosis based on image processing technology.

(ⅰ) Image acquisition.

(ⅱ) Lesion image segmentation.

(ⅲ) Feature extraction and normalization.

(ⅳ) Feature selection.

(ⅴ) Construction of disease recognition models.

Our image-processing techniques have been widely used for the identification of a variety of plant diseases. The accuracy of image-based plant disease identification depends largely on the segmentation of the lesion images. We provide image segmentation methods based on a fuzzy C-means clustering algorithm or a K-means clustering algorithm to perform lesion segmentation of plant disease images. In addition, we provide supervised classification methods to implement lesion segmentation of plant disease images efficiently. We are developing a system for alfalfa disease image recognition to achieve automatic recognition of the following alfalfa foliar diseases:

• Alfalfa common leaf spot disease (caused by Pseudopeziza medicaginis).
• Alfalfa rust (caused by Uromyces striatus).
• Alfalfa Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana).
• Alfalfa Tailspin leaf spot (caused by Cercospora medicaginis).

Why Choose Us

• A combination of clustering algorithm and supervised classification algorithm was used.
• Disease identification models were developed using pattern recognition methods, including random forest, SVM, and KNN.
• Customized service for diagnosis and identification of alfalfa foliar diseases.
• Development of optimal models for disease image recognition by comparing the recognition results of each model.
• Cutting-edge automated diagnosis system for alfalfa leaf diseases.

Disease spot images of different alfalfa diseases differ in color, texture, and shape. We use appropriate pattern recognition algorithms based on the color, texture, and shape features of lesion images for image recognition of alfalfa diseases. We look forward to working with you. For more information or to discuss in detail, please contact us.