Enhancing sugarcane disease classification with ensemble deep learning: A comparative study with transfer learning techniques

Daphal, Swapnil D.; Koli, S. M.

doi:10.1016/j.heliyon.2023.e18261

Cited by 11 publications

(4 citation statements)

References 35 publications

(32 reference statements)

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“…To evaluate designed system and to measure how efficient the model is performed, the metrics like recall, F1 score, accuracy, precision and root mean square value (RMS) [8]. The recall parameter is used to measure the ability of the model to every pertinent case in a given dataset and it may be defined as the ratio of actual predicted true positive values to the total dataset values and is defined by the equation 1 as:…”

Section: B Results and Discussionmentioning

confidence: 99%

“…In the work of Prithvi et al [7] implanted sugarcane disease prediction and pesticide recommendation system based on CNN technique and evaluated using accuracy parameter. Swapnil and Koli [8] has discussed different deep neural networks methodologies about the system for the detection of cane foliar diseases like rust, mosaic and spot of many crops.…”

Section: Literature Reviewmentioning

confidence: 99%

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CNN and LSTM Approach for Soil Nutrient analysis for Sugarcane Crop

2024

Preprint

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A new paradigm has been adopted in agricultural techniques, tools, and technology. To guarantee the implementation of site-specific crop management, which includes soil nutrient treatments according to crop requirements, precision agriculture is crucial. Soil nutrients are a major component in determining the growth of precision agriculture, which has gained global attention. One of the main challenges is more effective nutrient content detection as it guides well-planned nutrient-level-boosting routines. Many approaches, such using research labs and additional mobile labs, haven't shown to be very useful in helping farmers control their soil fertility. Taking advantage of the breakthroughs is severely hampered by factors like lack of knowledge and distance from research centers. In the work proposed, LSTM based RNN is employed to predict the Ph and nutrient values of the soil measured through microcontroller using sensor from the agricultural field. At the same time, RELU-CNN approach is applied to the soil images for measuring the same values. The values obtained from both the approaches are compared against each other so that the method may be made directly available to the farmers for evaluating the nutrient level of the soil and take necessary action. The approaches are measured in terms of quality parameters Recall, F1-Score, Accuracy, Precision and RMS value.

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Section: B Results and Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

CNN and LSTM Approach for Soil Nutrient analysis for Sugarcane Crop

2024

Preprint

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“…The system got accuracy up to 96.7%. Furthermore, AlexNet and GoogLeNet architectures for deep learning were used by Mohanty et al [24] to produce models for categorizing tomato leaf diseases. By combining learning methods and different training and testing splits, their system got accuracy of 99.35% by using PlantVillage [21] dataset.…”

Section: Related Workmentioning

confidence: 99%

“…al. [24] collected their own dataset of up to 2569 images with five different categories. The author used wellknown deep learning technique MobileNet-V2 proposing the system for better generalization and the system got accuracy of 84%.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Iftikhar,

Kandhro,

Kausar

et al. 2023

Preprint

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Farmers face a daunting challenge in meeting the escalating demands of a rapidly growing population for agricultural products, while plant diseases continue to exact a devastating toll on food production. Despite investing billions of dollars in disease management, agriculturists often struggle to achieve effective disease control without the support of advanced technology. The article explores a deep learning-based approach for disease detection. Specifically, it employs a Convolutional Neural Network (CNN) architecture for the detection. For the automated detection of plant disease, using plant images. This paper presents a new model for the early detection of plant detection based on processing plant images. And compare the in-depth performance analysis of hyper parameters in the context of plant disease detection by focusing on three distinct crops: (Apple, Corn, and Potato). Moreover, the data augmentation impact is analyzed. To enhance accessibility for farmers, our model is seamlessly integrated with a mobile application. The experimental results show the efficiency of our fine-tuned enhanced CNN model (E-CNN) achieving 98.17% accuracy on fungal classes. This research endeavors to pave the way for more effective plant disease management and ultimately to improve agricultural productivity in the face of mounting global challenges.

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