Apple Fruit Disease Detection and Classification Using K-Means Clustering Method

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

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Paecilomyces Rot: A New Apple Disease

Plant Disease ◽

10.1094/pdis-12-17-1896-re ◽

2018 ◽

Vol 102 (8) ◽

pp. 1581-1587 ◽

Cited By ~ 5

Author(s):

Megan N. Biango-Daniels ◽

Kathie T. Hodge

Keyword(s):

New York ◽

Apple Fruit ◽

Postharvest Disease ◽

Bitter Rot ◽

Golden Delicious ◽

Orchard Soil ◽

Apple Products ◽

Fruit Disease ◽

Bull's Eye ◽

Orchard Soils

Paecilomyces niveus is an important food spoilage fungus that survives thermal processing in fruit products, where it produces the mycotoxin patulin. Spoilage of products has been attributed to soil contamination; however, little is known about the ecology of this organism. In this study, orchard soils and culled apple fruit were surveyed and the ability of P. niveus to infect apple was tested on two popular apple varieties. P. niveus was found in 34% of sampled orchard soils from across New York. Completing Koch’s postulates, P. niveus was demonstrated to cause postharvest disease in Gala and Golden Delicious apple. Symptoms of this disease, named Paecilomyces rot, resemble several other apple diseases, including black rot, bitter rot, and bull’s-eye rot. External symptoms of Paecilomyces rot include brown, circular, concentrically ringed lesions, with an internal rot that is firm and cone-shaped. Both Gala and Golden Delicious apple fruit inoculated with P. niveus developed lesions ≥43 mm in size at 22 days after inoculation. There is some evidence that the size of lesions and rate of infection differ between Gala and Golden Delicious, which may indicate differing resistance to P. niveus. This work shows that P. niveus is common in New York orchard soil and can cause a novel postharvest fruit disease. Whether infected fruit can serve as an overlooked source of inoculum in heat-processed apple products requires further study.

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Application of Image Processing in Fruit and Vegetable Analysis: A Review

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0079 ◽

2015 ◽

Vol 24 (4) ◽

pp. 405-424 ◽

Cited By ~ 13

Author(s):

Shiv Ram Dubey ◽

Anand Singh Jalal

Keyword(s):

Image Processing ◽

Expert Knowledge ◽

State Of The Art ◽

Disease Classification ◽

Disease Detection ◽

Fruit And Vegetable ◽

Image Processing Techniques ◽

Agricultural Operations ◽

Fruit Disease ◽

Processing Techniques

AbstractImages are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed image-processing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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POMEGRANATE FRUIT DISEASES DETECTION USING IMAGE PROCESSING TECHNIQUES: A REVIEW

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i2.310 ◽

2021 ◽

Vol 9 (2) ◽

pp. 115-120

Author(s):

Jayashri Patil, Et. al.

Keyword(s):

Plant Root ◽

Modern Technology ◽

Plant Diseases ◽

Economic Losses ◽

Disease Detection ◽

Traditional System ◽

Detection Techniques ◽

Lower Accuracy ◽

Fruit Disease ◽

Pomegranate Fruit

The Agriculture plant diseases are responsible for farmer economic losses. These diseases affect on plant root, fruit, leaf, and stem. Detection of disease at early stages helps the farmer to improve productivity. In the traditional system agriculture experts and experienced farmer can recognize the plant diseases at the lower accuracy which causes losses to farmers. Currently several researchers are proposing soft computing and expert systems to recognize plant diseases. Plant disease identification by visual way is less accurate because some diseases do not have any visible symptoms or some of the diseases appear too late at the time of harvesting. The modern technology in agriculture sector can substantially improve the agriculture production & sustainability. This paper provides a review for fruit disease detection techniques for pomegranate plants. This study includes preprocessing, segmentation, feature extraction and classification techniques for pomegranate fruit diseases detection systems. This paper also states the comparison and limitations of existing fruit disease detection techniques.

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