Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder

Kulkarni, Pranesh; Wyawahare, Medha; Karwande, Atharva; Kolhe, Tejas; Kamble, Soham; Joshi, A.

doi:10.1007/978-3-031-07005-1_31

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…In this research, the SVM classifier predicts the diseases better than the minimum distance classifier. Kulkarni et al (2021) used two color features: gray level co‐occurrence matrix and HSV conversion. Later, the metrics from the random forest algorithm show that the model achieved more than 87% accuracy for all five plants.…”

Section: Literature Reviewmentioning

confidence: 99%

“…ML, deep learning (DL) and artificial intelligence techniques are capable of detecting diseases at early stages and helping farmers to deal with the diseases. The motivation for this research is to build a model which can predict the disease on the tomato leaves in the early stages (Kulkarni et al, 2021). Farmers have limited awareness of the technologies and innovations in the agriculture field and they need exposure to new technological innovations (Yadav et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

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Food security is a major concern in every developing country. Farmers face many problems while cultivating plants and they must take precautions at every stage of cultivation. Plants get diseases for various reasons like bacteria, insects, and fungus. Some diseases can be detected by examining the symptoms on the leaves. Early detection of diseases is a major concern and may require a thorough examination of the plants by an agricultural professional. This process is expensive and time taking. Machine learning (ML) algorithms help in image recognition and can be used to detect diseases on time without the need of an agricultural professional. In this project, the diseases in tomato leaves will be detected using image processing. The data from the images are extracted using different vectorization methods and classification algorithms like logistic regression (LR), support vector machine (SVM), and k‐nearest neighbors (KNN). Vectors of size 32 × 32 and 64 × 64 are used for training with normalizer scaling and no scaling. Out of the different approaches that were explored, SVM with the radial basis function (RBF) kernel gives the highest accuracy of 85% with no scaling and 64 × 64 image dimension.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

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“…The researchers of [30] applied random forest, an artificial neural strategy, to discern between healthy and damaged plants, and Contour of Focused Gradients was used to gather picture characteristics, and their method scored a recognition rate of 92. They deployed ML algorithms and computer vision technologies to evaluate and diagnose early vegetative infections in this report [31].…”

Section: Machine Learning For Plantlings Monitoringmentioning

confidence: 99%

A novel CNN gap layer for growth prediction of palm tree plantlings

Kumar,

Rajmohan,

Adeola Ajagbe

et al. 2023

PLoS ONE

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Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

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“…With the deep integration between deep learning and agricultural production, smart agriculture has become a major trend in the development of modern agriculture in different countries (Kamilaris and Prenafeta-Boldu, 2018;Nguyen et al, 2020). The use of cameras mounted on hardware devices to determine whether leaves are infected by pathogens has been widely used in the field of smart agriculture, leading to the automatic identification of crop diseases (Kulkarni et al, 2021;Gajjar et al, 2022). In recent years, increasing studies were focused in this field, Li et al (2022) developed an improved YOLOv5-S network to detect five vegetable diseases.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOX-Tiny network for detection of tobacco brown spot disease

Lin

Yu²,

Pan³

et al. 2023

Front. Plant Sci.

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IntroductionTobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs.MethodsHere, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network.ResultsAs a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS).DiscussionTherefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.

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Agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder

Cited by 4 publications

References 14 publications

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

A novel CNN gap layer for growth prediction of palm tree plantlings

Improved YOLOX-Tiny network for detection of tobacco brown spot disease

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