Adapted Approach for Fruit Disease Identification using Images

Anand Singh Jalal; Shiv Ram Dubey

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Adapted Approach for Fruit Disease Identification using Images

Shiv Ram Dubey, Anand Singh Jalal

Computer Vision and Pattern Recognition · Submitted 20 May 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,864 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.03 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.07 · mentions code or data availability 0.02.

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arXiv listing abstract v5. Results and the main claim are underlined; show every role.

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, an adaptive approach for the identification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot. Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide.	background 1.00	0.04	0.02	asserted 1.00
s1	In this paper, an adaptive approach for the identification of fruit diseases is proposed and experimentally validated.	contribution 1.00	0.80	0.07	asserted 0.98
s2	The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine.	method 1.00	0.79	0.03	asserted 0.99
s3	We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot.	method 0.67	0.95	0.03	asserted 0.97
s4	Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases.	interpretation 0.82	0.90	0.84	asserted 0.60
s5	The classification accuracy for the proposed solution is achieved up to 93%.	result 1.00	0.96	0.21	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read

arXiv listing abstract v5

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, an adaptive approach for the identification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot. Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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