Efficient crop management strategies and optimized agricultural practices are pivotal in maximizing overall yield of crops. An essential aspect of improving crop yield is tracking the phenological development of crops, which plays a crucial role in carrying out timely crop management activities, including irrigation, fertilization, pest control, and harvest. However, the lack of resources to acquire data for phenological detection in under-developed countries and the influence of climatic factors on phenology, pose significant challenges. Our work proposes a cost-effective methodology that harnesses the power of Earth Observation (EO) data to acquire essential ground data without the need to rely on manual collection. With a focus on South Asia region, we delve into the analysis of EO data for monitoring wheat phenology and its dynamic interactions with climatic factors. The study focuses on five wheat phenological stages (stem elongation, heading, medium milk, hard dough, and harvest) from 2020 to 2022. Breakpoint and extrema analysis following curve-fitting of Normalized Differential Vegetation Index (NDVI) from Sentinel-2 data accurately detects heading, medium milk, hard dough, and harvest with a one-to-three-day average difference for both years. Stem elongation is detected with a seven-day difference in 2021 and 2022. Furthermore, our analysis reveals that a significant temperature surge in 2022, coupled with minimal precipitation, causes an earlier maturation of the crop compared to in 2021. We thoroughly investigate this effect for 2021 and 2022 to assess the impact of the rate of change in weather conditions on wheat phenology. Embracing these findings can foster sustainable and productive agricultural practices.
Oral dysplasia is a pre-malignant stage of oral epithelial carcinomas, e.g., oral squamous cell carcinoma, where significant changes in tissue layers and cells can be observed under the microscope. However, malignancy can be reverted or cured using proper medication or surgery if the grade of malignancy is assessed properly. The assessment of correct grade is therefore critical in patient management as it can change the treatment decisions and prognosis for the dysplastic lesion. This assessment is highly challenging due to considerable inter- and intraobserver variability in pathologists’ agreement, which highlights the need for an automated grading system that can predict more accurate and reliable grade. Recent advancements have made it possible for digital pathology (DP) and artificial intelligence (AI) to join forces from the digitization of tissue slides into images and using those images to train and predict more accurate grades using complex AI models. In this regard, we propose a novel morphometric approach exploiting the architectural features in dysplastic lesions i.e., irregular epithelial stratification where we measure the widths of different layers of the epithelium from the boundary layer i.e., keratin projecting inwards to the epithelium and basal layers to the rest of the tissue section from a clinically significant viewpoint.
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