Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks

Septiarini, Anindita; Hamdani, Hamdani; Junirianto, Eko; Thayf, Moh. Sofyan S.; Triyono, Gandung; Henderi, - -

doi:10.1109/comnetsat56033.2022.9994555

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…The advancement of computer technology in agriculture has offered benefits to farmers, including increased productivity, efficiency, and competitiveness. Computer technology has been used in a variety of applications, including land management [1], fruit ripeness [2]- [4], and plant diseases [5]- [7]. Tea leaves may harbor plant diseases.…”

Section: Introductionmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

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Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.

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Section: Introductionmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

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Land morphology analysis with LiDAR technology to increase oil palm production

Wijayanti,

Hadi,

Tanjung

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The morphological condition of the land plays an important role in determining the quality of growth of oil palm plants. Integrated management of oil palm plantations is a key factor in increasing productivity. Analysis of land morphology in oil palm plantations is a crucial first step. The objectives of this study were (1) to characterize land morphology with LiDAR implementation, (2) to explain the LiDAR mechanism in the NDVI class classification used for determining the oil palm Vegetation Index, and (3) to provide ideas to optimize oil palm productivity. The study was conducted using the image interpretation method from the acquisition of LiDAR data which has a resolution of 3 x 3 cm in the form of DEM and orthophoto to be able to perform land surface morphology analysis (MPL) and NDVI flatfoot. The data needed in this study is Orthophoto, which is used to crosscheck field conditions, plant canopy conditions, and populations of plants per block. NDVI processing is used to determine the Vegetation Index to interpret the health of oil palm plants. The results showed that LiDAR technology can be used to determine the health of oil palm plants. The overall accuracy and reliability value of NDVI reached 88.33% and 88.13%, respectively. This shows that the value of NDVI can predict the health of oil palm plants and can be used to monitor them effectively.

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Utilizing deep learning via computer vision for agricultural production quality control: jackfruit growth stage identification

Krishnan,

Karuppasamypandiyan,

Chandran

et al. 2024

Eng. Res. Express

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Jackfruit (Artocarpus heterophyllus), a tropical fruit renowned for its diverse culinary uses, necessitates identifying the optimal growth stage to ensure superior flavor and texture. This research investigates employing deep learning techniques, particularly convolutional neural networks (CNNs), for accurately detecting jackfruit growth stages. Despite the challenge posed by the nuanced visual differences among fruits at various maturity stages, a meticulously curated dataset of labeled jackfruit images was developed in collaboration with experts, utilizing the BBCH scale. This dataset facilitated training and evaluation. A modified version of the Places 365 GoogLeNet CNN model was proposed for classifying four distinct growth stages of jackfruit, compared with a state-of-the-art CNN model. The trained models demonstrated varying levels of accuracy in classification. Furthermore, the proposed CNN model was trained and tested using both original and augmented images, achieving an impressive overall validation accuracy of 90%. These results underscore the efficacy of deep learning in automating the detection of growth stages, offering promising implications for quality control and decision-making in jackfruit production and distribution.

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Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks

Cited by 6 publications

References 26 publications

Classification of tea leaf disease using convolutional neural network approach

Classification of tea leaf disease using convolutional neural network approach

Land morphology analysis with LiDAR technology to increase oil palm production

Utilizing deep learning via computer vision for agricultural production quality control: jackfruit growth stage identification

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