An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves

Sangeetha, R.; Logeshwaran, J.; Rocher, Javier; Lloret, Jaime

doi:10.3390/agriengineering5020042

Cited by 46 publications

(2 citation statements)

References 29 publications

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“…As a comparison for metrics, Ref. [29] trained a deep learning model to identify Fusarium wilt and was only able to achieve 0.9156 accuracy, 0.9161 precision, 0.8856 recall, and 0.8156 score. Their model was also able to substantially outperform existing models with an accuracy nearly 0.20 higher than the second-best model compared in their study.…”

Section: Model Evaluationmentioning

confidence: 99%

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

Yan,

Shisher,

Sun

2023

AgriEngineering

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During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

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Section: Model Evaluationmentioning

confidence: 99%

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

Yan,

Shisher,

Sun

2023

AgriEngineering

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“…Timely remedial actions can prevent the spread of the disease. Over the past few years, the low production of these crops and fruits has led agriculturists to adopt modern agricultural methods [8]. One such method is the timely identification of disease on the plant and the recommendation of an effective treatment for it.…”

Section: Introductionmentioning

confidence: 99%

An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions

Noon,

Amjad,

Qureshi

et al. 2024

AgriEngineering

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Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.

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Identification of Banana Leaf Diseases: A Collaboration of Deep Learning Models

Anushya,

Shiwani

2024

Innovations in Sustainable Technologies and Computing

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An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves

Cited by 46 publications

References 29 publications

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions

Identification of Banana Leaf Diseases: A Collaboration of Deep Learning Models

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