Phytopathogenic nematodes associated with olive trees (Olea europaea L.) in North Africa: current status and management prospects

Laasli, Salah-Eddine; Mokrını, Fouad; Dababat, Abdelfattah A.; Yüksel, Ebubekir; İmren, Mustafa; Amiri, Saïd; Lahlali, Rachid

doi:10.1007/s41348-023-00744-6

Cited by 4 publications

(2 citation statements)

References 65 publications

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“…The output of the convolutional phase, denoted as Z, captures the high-level features extracted from the raw image. Equation (1) represents the feature extraction process.…”

Section: Prepared Datamentioning

confidence: 99%

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Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.

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“…The output of the convolutional phase, denoted as Z, captures the high-level features extracted from the raw image. Equation (1) represents the feature extraction process.…”

Section: Prepared Datamentioning

confidence: 99%

“…Olive trees are essential in many world regions, providing valuable products such as olive oil and table olives. However, olive trees are susceptible to several diseases that can significantly affect their health and productivity [1]. Early and accurate diagnosis of these diseases is critical for effective management and control [2].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Alsaeedi,

Al-juboori,

Al-Mahmood

et al. 2023

Sustainability

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Agroclimatic mapping for olive cultivation in Brazil: pinpointing optimal growing regions

de Oliveira Aparecido,

Torsoni,

de Lima

et al. 2024

J Sci Food Agric

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BACKGROUNDThis research aimed to identify the agroclimatic zones in Brazil, excluding Rio Grande do Sul, that are suitable for olive (Olea europaea L.) cultivation, considering both climatic and topographical factors. Olives require specific conditions: moderate winter temperatures (7–15 °C), warmer summers (25–35 °C) and sufficient water during growth and fruit maturation. They can endure some drought, making them a viable option for agricultural diversification. Using daily meteorological data from 1989 to 2023 from NASA‐POWER, this study analyzed variables like air temperature (minimum and maximum) and rainfall. Key climate variables were the mean air temperature in winter (T_w), spring (T_s), summer (T_su) and autumn (T_a) and total annual precipitation (Prec). Criteria for suitability included: T_w between 5 and 20 °C, T_s between 15 and 23 °C, T_su between 15 and 30 °C, T_a between 15 and 22 °C, annual precipitation over 900 mm and altitude below 900 m. Geographic information system software and Python 3.8 were employed for data analysis and zoning.RESULTSResults indicated that only 1.92% of the analyzed area, mainly in Minas Gerais, was suitable for olive cultivation. High temperatures and low rainfall in Brazil, particularly in the North and Midwest, make 59.56% of the country unsuitable for olive farming. Additionally, 18.58% of the land, mainly in the Northeast, faces challenges due to extreme heat (T_w) and insufficient water supply. © 2023 Society of Chemical Industry.

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Phytopathogenic nematode communities infesting Moroccan olive agroecosystems: impact of agroecological patterns

Laasli

Mokrını²,

Iraqi³

et al. 2023

Plant Soil

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Phytopathogenic nematodes associated with olive trees (Olea europaea L.) in North Africa: current status and management prospects

Cited by 4 publications

References 65 publications

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Agroclimatic mapping for olive cultivation in Brazil: pinpointing optimal growing regions

Phytopathogenic nematode communities infesting Moroccan olive agroecosystems: impact of agroecological patterns

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