A diverse ensemble classifier for tomato disease recognition

Astani, Mounes; Hasheminejad, Mohammad; Vaghefi, Mahsa

doi:10.1016/j.compag.2022.107054

Cited by 52 publications

(12 citation statements)

References 24 publications

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“…Another suggestion in (Astani et al, 2022) was to evaluate the e cacy of the Ensemble Classi er in classifying plant diseases using datasets such as Plant Village and Taiwan Tomato Leaves. Resilient convolutional neural network in (Gokulnath et al, 2021) failed to perform well with real-time photographs collected in different environmental conditions using Tomato leaf disease detection benchmark dataset, indicating limitations in its applicability.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Early Detection of Tomato Leaf Disease using IoT and Deep Learning

Arafat,

Aswath,

Nishath

2024

Preprint

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Tomato plants are defenseless to different illnesses, including bacterial, contagious, and viral contaminations, which can fundamentally lessen crop yield and quality on the off chance that not identified and treated early. Farmers may experience increased crop damage and financial losses as a result of this detection delay. The goal is to foster a robotized framework utilizing IoT (Internet of Things) gadgets, for example, cameras conveyed in the field, joined with profound learning strategies, to precisely and quickly distinguish illnesses in tomato plants. This framework intends to give ranchers an early admonition framework that can recognize and order infections quickly, empowering convenient intercession and designated treatment, accordingly further developing harvest wellbeing and yield. Profound learning has essentially expanded the precision of picture classification and article identification frameworks' acknowledgment as of late. The exploration zeroed in on computerizing the early location of tomato leaf sicknesses utilizing IoT innovation and a changed ResNet50 profound learning model. At first, IoT gadgets, including sensors and cameras, were conveyed in tomato fields to gather plant-related information and pictures. We focused on calibrating the hyper boundaries of pre-prepared models, including GoogLeNet, SquezeNet and ResNet-50. The notable Tomato leaf disease detection dataset, which incorporates 3,890 picture tests of different sickness and healthy leaves, was utilized for the tests. Using comparable cutting-edge research, a comparative analysis was also conducted. The tests showed that ResNet-50 outflanked cutting edge models with a 99.87% more prominent characterization exactness. The framework demonstrated commendable capability in identifying whether tomato plant leaves were affected by disease in their early stages. This capability enabled farmers to receive timely alerts through mobile application, allowing for more effective management of the issue.

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Section: Literature Surveymentioning

confidence: 99%

Automatic Early Detection of Tomato Leaf Disease using IoT and Deep Learning

Arafat,

Aswath,

Nishath

2024

Preprint

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“…[46,47]. In the field of smart agriculture, researchers often use geometric transformation and pixel intensity shift (color shift) for data augmentation [48][49][50]. As shown in Figure 2, data augmentation processes were randomly performed by horizontal flip, contrast enhancement and brightness enhancement to all 450 images for simulating the effects of different weather and light intensities, with the aim of improving the robustness and the generalization ability of the recognition model.…”

Section: Data Preprocessingmentioning

confidence: 99%

Improved U-Net for Growth Stage Recognition of In-Field Maize

et al. 2023

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Precise recognition of maize growth stages in the field is one of the critical steps in conducting precision irrigation and crop growth evaluation. However, due to the ever-changing environmental factors and maize growth characteristics, traditional recognition methods usually suffer from limitations in recognizing different growth stages. For the purpose of tackling these issues, this study proposed an improved U-net by first using a cascade convolution-based network as the encoder with a strategy for backbone network replacement to optimize feature extraction and reuse. Secondly, three attention mechanism modules have been introduced to upgrade the decoder part of the original U-net, which highlighted critical regions and extracted more discriminative features of maize. Subsequently, a dilation path of the improved U-net was constructed by integrating dilated convolution layers using a multi-scale feature fusion approach to preserve the detailed spatial information of in-field maize. Finally, the improved U-net has been applied to recognize different growth stages of maize in the field. The results clearly demonstrated the superior ability of the improved U-net to precisely segment and recognize maize growth stage from in-field images. Specifically, the semantic segmentation network achieved a mean intersection over union (mIoU) of 94.51% and a mean pixel accuracy (mPA) of 96.93% in recognizing the maize growth stage with only 39.08 MB of parameters. In conclusion, the good trade-offs made in terms of accuracy and parameter number demonstrated that this study could lay a good foundation for implementing accurate maize growth stage recognition and long-term automatic growth monitoring.

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“…There are various technologies, such as machine learning and computer vision are implemented to develop such systems [14]. Nanostructure-based non-invasive detection tools, coupled with portable imaging devices such as smart phones, have enabled quick and on-site plant diseases diagnosis, allowing for long-term monitoring of plant health conditions, particularly in resource-limited areas [15].…”

Section: Introductionmentioning

confidence: 99%

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Kanagaraj,

Kulandaivel,

Shajin

et al. 2024

Preprint

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The emergence of new diseases on plant leaves poses a substantial threat to global food safety and agricultural productivity. To mitigate this risk, accurate and swift detection of plant illnesses is crucial, reducing unnecessary expenses and minimizing financial losses and environmental damage. This study proposes a method called Plant Leaf Disease Detection with a Constitutive Artificial Neural Network (PLDD-CANN) to provide advancements in deep learning. The approach begins by gathering data from the Plant Village dataset and subjecting it to pre-processing techniques. This includes noise removal and image enhancement using a Variational Marginalized Particle Filter (AVMPF). Next, an Adaptive Convex Clustering (ACC) method is employed for image segmentation, followed by feature extraction using Fast Fourier and Continuous Wavelet (FFCWT) transforms. Finally, a Constitutive Artificial Neural Network (CANN) is utilized to categorize the input image to one of several categories, including healthy and various disease types like Yellow Leaf Curl Virus, Septoria Leaf Spot, Two-Spotted Spider Mite, Bacterial Spot, Target Spot, Leaf Mold, Mosaic Virus, Early Blight, and Late Blight. Then, the proposed technique is simulated using Python under several performance metrics including precision, f1-score, error rate accuracy, sensitivity, specificity and ROC. The proposed PLDD-CANN method provides 26.75%, 25.83% and 27.46% higher accuracy comparing with existing methods an enhanced CNN technique for plant leaves disease diagnosis in tomato (CNN-PLDD), A Novel Approach for Plant Leaf Disease Predictions with Recurrent Neural Network RNN Classification Method (RNN-PLDD), Detection of tomato leaf diseases for agro-based industries (FRCNN-PLDD) respectively.

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A diverse ensemble classifier for tomato disease recognition

Cited by 52 publications

References 24 publications

Automatic Early Detection of Tomato Leaf Disease using IoT and Deep Learning

Automatic Early Detection of Tomato Leaf Disease using IoT and Deep Learning

Improved U-Net for Growth Stage Recognition of In-Field Maize

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

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