From leaf to harvest: achieving sustainable agriculture through advanced disease prediction with DBN‐EKELM

Background In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, the...

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Vydáno v:	Journal of the science of food and agriculture Ročník 104; číslo 13; s. 8306 - 8320
Hlavní autoři:	Rajasekar, Deepa, Moorthy, Vaishnavi, Rajadurai, Priscilla, Ravikumar, Sethuraman
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Chichester, UK John Wiley & Sons, Ltd 01.10.2024 John Wiley and Sons, Limited
Témata:	Accuracy Agricultural industry Agriculture - methods Algorithms Belief networks Classification classification accuracy Crops, Agricultural - growth & development Deep Learning Disease detection Economics Effectiveness enhanced kernel extreme learning machine Growing season Image enhancement Image quality Learning algorithms Leaves Machine Learning novel binary gaining sharing knowledge‐based optimization algorithm Parameter identification Parameter sensitivity plant disease detection Plant diseases Plant Diseases - microbiology Plant extracts Plant Leaves - chemistry Plants Sensitivity analysis Sustainable agriculture classification accuracy plant disease detection enhanced kernel extreme learning machine novel binary gaining sharing knowledge‐based optimization algorithm
ISSN:	0022-5142, 1097-0010, 1097-0010
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Background In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. Method The objective of this study is to develop a new deep learning‐based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network‐based enhanced kernel extreme learning machine (DBN‐EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN‐EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge‐based optimization algorithm (NBGSK). Result The efficacy of the proposed DBN‐EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F‐measure. Conclusion Experimental analyses demonstrate that the DBN‐EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry.
Bibliografie:	Correction added after first online publication on 20 August 2024; Priscilla Rajadurai's department name in affiliation has been updated. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3
ISSN:	0022-5142 1097-0010 1097-0010
DOI:	10.1002/jsfa.13665