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Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination of crop types. This systematic review examines the evolution of hyperspectral platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors to space-borne satellites (e.g., EnMAP, PRISMA), and explores recent scientific advances in AI methodologies for crop mapping. A review protocol was applied to identify 47 studies from databases of peer-reviewed scientific publications, focusing on hyperspectral sensors, input features, and classification architectures. The analysis highlights the significant contributions of Deep Learning (DL) models, particularly Vision Transformers (ViTs) and hybrid architectures, in improving classification accuracy. However, the review also identifies critical gaps, including the under-utilization of hyperspectral space-borne imaging, the limited integration of multi-sensor data, and the need for advanced modeling approaches such as Graph Neural Networks (GNNs)-based methods and geospatial foundation models (GFMs) for large-scale crop type mapping. Furthermore, the findings highlight the importance of developing scalable, interpretable, and transparent models to maximize the potential of hyperspectral imaging (HSI), particularly in underrepresented regions such as Africa, where research remains limited. This review provides valuable insights to guide future researchers in adopting HSI and advanced AI models for reliable large-scale crop mapping, contributing to sustainable agriculture and global food security.

Africa’s rapidly growing population is driving unprecedented demands on agricultural production systems. However, agricultural yields in Africa are far below their potential. One of the challenges leading to low productivity is Africa‘s poor soil quality. Effective soil fertility management is an essential key factor for optimizing agricultural productivity while ensuring environmental sustainability. Key soil fertility properties—such as soil organic carbon (SOC), nutrient levels (i.e., nitrogen (N), phosphorus (P), potassium (K), moisture retention (MR) or moisture content (MC), and soil texture (clay, sand, and loam fractions)—are critical factors influencing crop yield. In this context, this study conducts an extensive literature review on the use of hyperspectral remote sensing technologies, with a particular focus on freely accessible hyperspectral remote sensing data (e.g., PRISMA, EnMAP), as well as an evaluation of advanced Artificial Intelligence (AI) models for analyzing and processing spectral data to map soil attributes. More specifically, the study examined progress in applying hyperspectral remote sensing technologies for monitoring and mapping soil properties in Africa over the last 15 years (2008–2024). Our results demonstrated that (i) only very few studies have explored high-resolution remote sensing sensors (i.e., hyperspectral satellite sensors) for soil property mapping in Africa; (ii) there is a considerable value in AI approaches for estimating and mapping soil attributes, with a strong recommendation to further explore the potential of deep learning techniques; (iii) despite advancements in AI-based methodologies and the availability of hyperspectral sensors, their combined application remains underexplored in the African context. To our knowledge, no studies have yet integrated these technologies for soil property mapping in Africa. This review also highlights the potential of adopting hyperspectral data (i.e., encompassing both imaging and spectroscopy) integrated with advanced AI models to enhance the accurate mapping of soil fertility properties in Africa, thereby constituting a base for addressing the question of yield gap.

Pheochromocytomas are rare neuroendocrine tumors arising from chromaffin cells of the adrenal medulla or extra-adrenal paraganglia. Their clinical presentation varies widely among individuals and is primarily linked to the biological effects of excessive catecholamine secretion. We report an unusual case of pheochromocytoma presenting as epigastric heaviness. A 65-year-old male with a known history of type 2 diabetes mellitus, treated with metformin, and well-controlled hypertension managed with amlodipine, presented with a complaint of persistent epigastric heaviness in the absence of associated symptoms. Abdominal CT imaging revealed a large, locally infiltrative right adrenal mass measuring 92 x 82 x 80 mm. As part of the etiological work-up, serum potassium and urinary cortisol levels were within normal limits. The diagnosis of pheochromocytoma was established based on significantly elevated urinary metanephrines and normetanephrines, exceeding the normal values by 55-fold and 23-fold, respectively. The evaluation for multiple endocrine neoplasia syndromes was negative. Iodine-123 metaiodobenzylguanidine (¹²³I-MIBG) scintigraphy confirmed the presence of a hyperfixating adrenal mass, consistent with pheochromocytoma. Following adequate preoperative pharmacologic preparation, the patient underwent a right adrenalectomy. Histopathological analysis confirmed the diagnosis of pheochromocytoma. The postoperative course was uneventful. Pheochromocytomas are most often benign but may be associated with severe cardiovascular complications due to catecholamine excess. Their clinical diagnosis remains challenging due to the lack of specific signs and symptoms. The diagnosis relies on biochemical assays of catecholamine metabolites, supported by functional and anatomical imaging techniques. Surgical excision, preceded by meticulous pharmacologic preparation, remains the cornerstone of treatment. Early diagnosis and appropriate management are essential to prevent potentially life-threatening complications and ensure favorable outcomes.