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https://bentham-peer-review-service-017.net/review-request-declined.php?au=82239660&Camp=116749
Submission: On December 21 via api from US — Scanned from US
Submission: On December 21 via api from US — Scanned from US
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1 forms found in the DOMPOST decline-acknowledgement-confirmation.php
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<h4 class="m-b-10">Reason for Declining the Review Request:</h4>
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Text Content
JOURNAL OF INTELLIGENT SYSTEMS IN CURRENT COMPUTER ENGINEERING * Agree to Review * Declined to Review * Unsubscribe from the List PEER REVIEW REQUEST - DECLINE CONFIRMATION BMS-ISCCE-2024-6 -------------------------------------------------------------------------------- Article Title: A Novel Real-Time Deep Learning Method for Identifying Potato Plant Diseases Abstract: Plant Disease detection is a major challenge in the field of food production as well as food security. Artificial intelligence and soft computing tools can play a vital role in this arena as indicated by many researchers. Plant leaf is an important part to detect plant disease and Potato is a major crop specially in the developing country like India where the Indian common people are very much dependent for their carbohydrate supplement. But the diseases like “Early Blight” and “Late Blight” affect the quality as well as quantity of the potato production very much. Through manual inspection it is very difficult as well as time consuming job to detect this type of diseases. But through image processing especially using deep learning it is very promising as well as modern approach to detect such kind of plant disease effectively and efficiently. In this paper, a novel deep leaning approach (modified Dense-Net) has been used to detect potato plant disease in the country like India. In this paper the researcher emphasized on mainly developing a novel deep learning method by modifying the Dense-Net deep learning architecture. After a rigorous study it is found that in the year 2020, on the same dataset Tiwari et al. achieved the classification accuracy up to 97.8%. The researchers of this paper acquired 99.54% classification accuracy, this significant margin will obviously a promising classification approach to detect the diseases accurately. This will help the farmers to classify the diseases to achieve a reliable result. Furthermore, to facilitate the farmer a user-friendly mobile app powered by advanced AI a deep learning algorithm. The app offers prediction and diagnosis of various potato plant diseases and actionable solution to address all the potato plant health concerns. Additionally, it facilitates direct communication with certified agronomists and plant experts via messaging for expert advice and consultations. REASON FOR DECLINING THE REVIEW REQUEST: Choose Reason: Unavailability of time Article is out of expertise -------------------------------------------------------------------------------- I confirm that I must decline your invitation this time around because of the above reason -------------------------------------------------------------------------------- IF YOU WISH TO RECOMMEND ANYONE WHO COULD REVIEW THIS MANUSCRIPT, THEN PLEASE PROVIDE THE DETAILS AS FOLLOWS: I would like to suggest the reviewer for this manuscript SUGGESTED REVIEWER'S CONTACT DETAILS: First Name: Last Name: Affiliation: Country: Email Address: Copyright 2023 © Peer Review Service