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JOURNAL OF INTELLIGENT SYSTEMS IN CURRENT COMPUTER ENGINEERING


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PEER REVIEW REQUEST - ACCEPTANCE CONFIRMATION


BMS-ISCCE-2024-6

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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.

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