Nineteen thrips species recorded in center of Iraq during 1999-2001, four of them was recorded by El-Haidari & Daoud, 1967; Thrips tabaci Lindeman, Retithrips syriacus (Mayet), Parascolothrips prieseri Mound, Anaphthrips sudanensis Trybom. Fifteen species are recorded for the first time in Iraq, Thrips meridionalis (Priesner), Microcephalothrips abdominals (Crawford), Scolothrips pallidus (Beach), Scolothrips sexmaculatus (Pergande), Scritothrips mangiferae Priesner, Frankliniella schultzie Trybom, Frankliniella unicolor Morgan, Frankliniella Tritici Bagnall, Retithrips aegypticus Marchal, Retithrips javanicus

Four new species of Thrips (Thripidae) Chirothrips imperatus sp. nov.; Frankliniella megacephala sp. nov.; Retithrips bagdadensis sp. nov; Taeniothrips tigridis sp. Nov.; from middle of Iraq, are described and illustrated with their hosts.

An attempt was made to determine the insect parasites of cockroaches in Iraq. As a result of this survey three species of Hymenoptera representing two separate families, which have been reared from ootheca of cockroaches were recovered. These were: Evania dimidiata Fabricius, Evania appendigaster (Linnaeus) (Evaniidae) and Anastatus longicornis sp. n. (Eupelmidae) which described here as a new species from Iraq.

The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthe

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co