Cotton Plant Disease Prediction using Deep Learning

Sreeja, S P; Asha, V; Saju, Binju; Chandrakantbhai, Paunikar Priti; Prabhasan, Pramrish; Prasad, Arpana

doi:10.1109/c2i456876.2022.10051527

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…The significance of automated disease detection in cotton plants using CNNs lies in its transformative impact on agriculture [12]. To maximise yields and practise sustainable crop management, it is imperative to promptly identify plant diseases.…”

Section: Importance Of Cotton In Agriculturementioning

confidence: 99%

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

Parikshit N. Mahalle

2024

jes

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One of the most important crops for economic survival is cotton, and one of the biggest challenges it faces is early disease detection that affect productivity. The cotton business may suffer financial losses because of the frequently insufficient visual detection of these diseases by humans. This study presents an intelligent approach for the detection of cotton plant diseases using Convolutional Neural Networks (CNNs) with a focus on ResNet-152V2 architecture. Leveraging deep learning techniques, specifically ResNet-152V2, the model exhibits robust performance in identifying various diseases affecting cotton plants. The research involved training the model on a diverse dataset encompassing different cotton leaf diseases. Results demonstrate a better accuracy, with the proposed approach achieving an impressive precision in disease detection. The utilization of ResNet-152V2 enhances the model's capability to accurately classify and diagnose cotton plant diseases, showcasing its efficacy for real-world applications. The study contributes to the advancement of automated disease detection systems in agriculture, particularly in the context of cotton crops.

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Section: Importance Of Cotton In Agriculturementioning

confidence: 99%

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

Parikshit N. Mahalle

2024

jes

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Comparative and Comprehensive Analysis of Cotton Crop Taxonomy Classification

Wagh,

Dobariya

2024

Communications in Computer and Information Science

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Study on UPQC Integration Benefits in a Hybrid Solar Wind Energy System

Baswaraju,

Siva Prasada Raju,

Sobti

et al. 2024

E3S Web Conf.

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The researchers are focusing on harnessing the electricity form the renewable energy resources by overcoming the challenges faced by them due its variable nature. This study discusses the developing significance of renewable energy sources (RES), in particular wind and solar electricity, in meeting future electricity demands. India’s geographical length is highlighted as beneficial for integrating variable renewable energy outputs into its grid. The paper also addresses Power-quality (PQ) issues springing up from integrating renewable energy sources, proposing solutions including single and Unified active power filter (SUAPF) and distributed Static Compensators (DSTATCOM). The work examines power quality issues associated with grid-related solar PV systems and the combination of Unified Power Quality Conditioners (UPQCs) in hybrid solar-wind systems. The analysis of the UPQC integration with the solar wind energy based hybris system concludes the enhancement in the power quality issues such as total harmonic distortions along with balancing the reactive power in the line.

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Cotton Plant Disease Prediction using Deep Learning

Cited by 6 publications

References 10 publications

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

Comparative and Comprehensive Analysis of Cotton Crop Taxonomy Classification

Study on UPQC Integration Benefits in a Hybrid Solar Wind Energy System

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