Rainy days and rainfall intensity trends in Basar (Arunachal Pradesh) during 1979-2015

Bhagawati, Kaushik; Sen, Amit; Kalita, H.; Bhagawati, R.; Shukla, Kshitiz Kumar

doi:10.54302/mausam.v69i3.333

Cited by 4 publications

(1 citation statement)

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“…Recently, DL approaches are mostly employed for plant diseases prediction and classification, due to its effective performance. DL algorithms produce an output through the multiple layers by extracting the complicated features of leaf images (Bhagawati et al, 2015;Lu et al, 2017;Anandkumar, 2020).…”

Section: Significancementioning

confidence: 99%

Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

Daniya¹,

Vigneshwari²

2023

J ENV INFORM

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An automatic identification and classification of rice diseases are very important in the domain of agriculture. Deep learning (DL) is an effective research area in the identification of agriculture pattern identification where it can effectively resolve the issues of diseases identification. In this paper, a hybrid optimization algorithm is developed to categorize the plant diseases. The pre-processing is made using Region of Interest (ROI) extraction and the input image is created by combining the Rice plant dataset, and Rice disease dataset. The segmentation is accomplished using Deep fuzzy clustering. The features, like statistical features, entropy, Convolutional Neural Network (CNN) features, Local Optimal-Oriented Pattern (LOOP), and Local Gabor XOR Pattern (LGXP) is considered for extracting the appropriate features for further processing. The data augmentation is employed to enlarge the volume of extracted features. Then, the first level classification is made by deep neuro-fuzzy network (DNFN), which is trained using Rider Henry Gas Solubility Optimization (RHGSO) that categories into healthy and unhealthy plants. The RHGSO is the integration of Rider Optimization Algorithm (ROA) and Henry gas solubility optimization (HGSO). After that, second-level classification is made by a Deep residual network (DRN) that is tuned by RHGSO. Thus, the RHGSO-based DRN categorizes the unhealthy plants into Bacterial Leaf Blight (BLB), Blast, and Brown spot. Thus, the implementation of the proposed RHGSO-based deep learning approach offered better accuracy, sensitivity, specificity, and F1-score of 0.9304, 0.9459, 0.8383, and 0.9142.

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Section: Significancementioning

confidence: 99%