The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.

We present a common framework for dialectical proof procedures for computing credulous, grounded, ideal and sceptical preferred semantics of abstract argumentation. The framework is based on the notions of dispute derivation and base derivation. Dispute derivation is a dialectical notion first introduced for computing credulous semantics in assumption-based argumentation, and adapted here for computing credulous semantics and grounded semantics. Base derivation is introduced for two purposes: (i) to characterize all preferred extensions containing a given argument, and (ii) to represent backtracking in the search for a dispute derivation. We prove the soundness of the proof procedures for any argumentation frameworks and their completeness for general classes of finitary or finite-branching argumentation frameworks containing the class of finite argumentation frameworks as a subclass. We also discuss related results.

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Background Craniopharyngiomas are common lesions that occur in the suprasellar region; however, strictly intrinsic third ventricular craniopharyngiomas are rare. Case Series We aimed to describe the magnetic resonance imaging features observed in five cases of strictly intrinsic third ventricular papillary craniopharyngiomas, including two cases of mixed cystic and solid tumors and three cases of pure solid masses. Conclusion Among the adult population, intrinsic third ventricular papillary craniopharyngiomas should be considered when either solid or mixed cystic and solid masses are observed, in which the solid component shows heterogeneous intensity, heterogeneous and strong enhancement, and is strictly located in the third ventricle.

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Gliosarcoma (GS) is an uncommon central nervous system tumor with several characteristics of a malignant neoplasm and poor prognosis. The majority of GS reports describe a predilection for the cerebral hemispheres, and cases of intraventricular GS are extremely rare, with only a few reported. In addition, intraventricular GS has not been associated with any unique radiographic or clinical features, which can result in misdiagnosis as other intraventricular tumor types. In this report, we present the case of a 32-year-old woman with GS in the trigone of the lateral ventricle and provide a retrospective review of similar, previously reported cases.

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