No abstract

Although jute (Corchorus olitorius L.) is treated as a weed in Turkey, it is cultivated and harvested for its fiber, tender shoots, and leaves in Africa and Asia. We report the occurrence and symptomatology of phyllody disease in jute observed for the first time in 2010 during our studies focusing on sesame phyllody in an experimental field at the Akdeniz University Campus, Antalya, Turkey. The disease was also observed in the following two years, 2011 and 2012. In the top of the infected jute plant, the internodes were shortened which resulted a cluster of leaves in smaller size than the normal ones, and the leaves were crinkled as well as turned to yellowing and leathery-looking. Additionally, the large leaves accumulated more anthocyanin in their margins. The floral organs abnormally developed into leafy structures; and ovaries at the symptomatic part enlarged but stamens and filaments did not show any symptoms. There was neither proliferation of the branches nor needle-like shape of the leaves in our case. Jute and sesame seeds started germination synchronously, and looked similar at the cotyledonary stage. Wild plants or weeds deserve a particular attention for disease development or inoculum build-up in cultivated crops. Considering the voluntary nature, jute may be an alternative for biofuel production. Also, the similarity in developmental stages of jute and sesame suggests that they might be affected by the same phytoplasma. To verify this, molecular analyses have been started.

This study aims to use contaminated soil with leachate to select autochthonous fungi that are able to bioremediate three types of leachate, (Young (YL), Intermediate (IL) and Old (OL)). Eleven fungal species were isolated via the enrichment method using the leachate as the sole source of carbon and energy. The isolates were evaluated for their ability to grow and remove organic pollutants at 100%, 50% and 25% (v/v) of leachate in both solid and liquid cultures that were spiked with malt extract. The results indicated that only three fungi, <i>Aspergillus flavus</i> (<i>A. flavus</i>-LC106118), <i>Aspergillus niger</i> (<i>A. niger</i>-KT192262) and <i>Fusarium solani</i> (<i>F. solani</i>-KX349467) showed significantly high capacity to grow on leachate, with maximum radial growth rates (Gr) of 7.5 mm, 4.7 mm , and 5.3 mm, respectively. In addition, 34%, 22%, and 27%, respectively of COD removal rates were obtained at 25% concentration in YL. A. flavus was the most tolerant fungus against landfill leachate, followed by <i>F. solani</i>, and finally <i>A. niger</i>. Therefore, these three fungi are good candidates for leachate bioremediation. However, for a better remediation, the combined effects of different types of fungi and leachates on the fungal growth need to be considered during the fungi selection.

RésuméLes conditions de régénération de Zornia glochidiata Reichb. ex DC., une légumineuse locale à bonne valeur fourragère et bien appétée par le bétail, ont été étudiées au laboratoire sur des graines récoltées dans la zone sylvopastorale du Sénégal. Trois types de prétraitements, mécanique, chimique et thermique, ont été utilisés pour améliorer la germination des graines. Les pré-traitements mécanique et chimique ont augmenté significativement la germination des graines avec respectivement des taux de germination de 86 et 96 p. 100 comparés au taux de 25 p. 100 obtenu chez les graines non prétrai-tées. Le meilleur taux de germination a été obtenu avec des graines mises à germer à la température de 25 °C. Les chocs thermiques d'amplitude faible à moyenne (50, 60 et 80 °C pendant 24 h) ont conservé le pouvoir germinatif des graines, mais les taux de germination enregistrés ont été similaires à celui du témoin (25 p. 100). Les chocs thermiques élevés (100, 125 et 150 °C pendant 5, 10 et 15 min) ont inhibé totalement la germination des graines.

Mango is one of the most traded fruits in the world. Therefore, mango production suffers from several pests and diseases which reduce the production and quality of mangoes and their price in the local and international markets. Several solutions for automatic diagnosis of these pests and diseases have been proposed by researchers in the last decade. These solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In recent years, Convolutional Neural Networks (CNNs) have achieved impressive results in image classification and are considered as the leading methods for image classification. However, one of the most significant issues facing mango pests and diseases classification solutions is the lack of availability of large and labeled datasets. Data augmentation is one of solutions that has been successfully reported in the literature. This paper deals with data augmentation techniques namely blur, contrast, flip, noise, zoom and affine transformation to know, on the one hand, the impact of each technique on the performance of a ResNet50 CNN using an initial small dataset, on the other hand, the combination between them which gives the best performance to the DL network. Results show that the best combination classifying mango leaf diseases is ‘Contrast & Flip & Affine transformation’ which gives to the model a training accuracy of 98.54% and testing accuracy of 97.80% with an f1_score > 0.9.