Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

Shi, Yue; Han, Liangxiu; Kleerekoper, Anthony; Chang, Sheng; Hu, Tongle

doi:10.3390/rs14020396

Cited by 42 publications

(11 citation statements)

References 62 publications

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“…To overcome the limitations of electronic sensors, a second level of processing is introduced using image spectrograph technology. Hyperspectral images of the tea leaves are collected and processed using deep learning techniques to identify the tea category type [20]. A tea leaf recognition model is developed utilizing a CNN model, and the classification of tea leaves is performed based on their extracted features, employing a random forest (RF) classification model [21].…”

Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model

Gongalla,

Bordoloi

2024

IJEECS

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Tea, a commonly consumed beverage, is susceptible to being sold in adulterated or expired forms by third-party vendors. Hyperspectral imaging across different wavelength bands has proven to precisely assess the diverse types of tea and their corresponding financial gains. This study aims to employ a deep learning methodology in conjunction with hyperspectral imaging for efficiently classifying tea leaves. A novel approach is proposed, wherein a waveband convolutional neural network is utilized to generate hyper spectral images of tea leaf samples with enhanced resolution. The model known as optimized-convolutional neural network-random forest O- (ConvNet-RF) demonstrated exceptional performance, achieving high accuracy, impressive recall, F1 score, and notable sensitivity rate, outperforming existing alternative methods. The tea leaf types, namely green, yellow, and black, were accurately identified using a combination of the random forest (RF) model and the O-ConvNet-RF model. The tree-based classification method for the identification of tea leaves demonstrated superior performance as compared to alternative machine learning models. In general, this study presents a successful methodology for the classification of tea leaves, with potential implications for consumer processing and distributor profit analysis.

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Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model

Gongalla,

Bordoloi

2024

IJEECS

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“…Plant disease classification [1] Plant disease segmentation subtype identification, and survival probability estimation [2] Plant disease detection and classification [3] Crop disease and nutrient deficiency detection [4] Potato late blight disease detection [5] Plant disease temporal tracking [6] Machine Learning Plant disease detection [7]…”

Section: Deep Learningmentioning

confidence: 99%

Advancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis

Sola Mohana Durga Venkata Sri

2024

jes

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Plant diseases have the potential to damage the livelihoods of farmers and impede their capacity to generate an adequate quantity of food. Diagnosis and early detection of plant diseases are critical for their effective management and control. Leaf analysis shows great potential as a method for forecasting disease stages due to its ability to detect subtle alterations in leaf physiology and appearance that may occur prior to the onset of conspicuous symptoms. By utilizing the algorithms of machine learning and deep learning, it is possible to classify characteristics extracted from photographs of leaves into distinct disease stages. Recent studies have demonstrated the potential of these algorithms, as they have achieved remarkable accuracy in disease stage prediction despite having limited training data. The integration of machine learning and deep learning techniques with foliage analysis holds promise for revolutionizing plant disease management through the provision of timely identification, accurate diagnosis, and customized treatment. In order to formulate efficacious disease management strategies, precise determination of the developmental stage of plant diseases is imperative. Scholars are presently devising novel approaches to identify the stages of plant diseases by employing diverse methodologies, including spectroscopy, machine learning, and image processing. This can benefit producers in substantial economic and environmental ways, as well as contribute to the improvement of food security. The primary investigation comprises an assortment of articles spanning the years 2014 to 2023. After evaluating various search strategies, a total of 117 research publications were identified, of which 43 were pertinent. The article examines numerous developments in deep learning research. In addition, it will facilitate the assessment of the present and prospective state of plant disease research by employing deep learning methodologies.

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“…According to Table 3 , CNN and RF provide better results than MLP and SVR achieving an R2 of 0.74 for CNN and 0.75 for RF. Similarly, the authors in Shi et al [ 65 ] developed a 3D-CNN model called CropdocNet to detect potato Late Blight disease from hyperspectral images collected using a UAV platform. The proposed model provided impressive results achieving an average accuracy of around 98% and 96% on the training and independent testing sets.…”

Section: Deep Learning Algorithms To Identify Crop Diseases From Uav-...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

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Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

Cited by 42 publications

References 62 publications

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model

Advancements in Crop Disease Detection: Analytical Methods for Recognizing Disease Stages through Leaf Analysis

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

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