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Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively., (© 2024. The Author(s).)

Purpose: Obstructive sleep apnea (OSA) is associated with increased risk for stroke, which is known to further impair respiratory functions. However, it is unknown whether the type and severity of respiratory events are linked to stroke or transient ischemic attack (TIA). Thus, we investigate whether the characteristics of individual respiratory events differ between patients experiencing TIA or acute ischemic stroke and matched patients with clinically suspected sleep-disordered breathing., Methods: Polygraphic data of 77 in-patients with acute ischemic stroke (n = 49) or TIA (n = 28) were compared to age, gender, and BMI-matched patients with suspected sleep-disordered breathing and no cerebrovascular disease. Along with conventional diagnostic parameters (e.g., apnea-hypopnea index), durations and severities of individual apneas, hypopneas and desaturations were compared between the groups separately for ischemic stroke and TIA patients., Results: Stroke and TIA patients had significantly shorter apneas and hypopneas (p < 0.001) compared to matched reference patients. Furthermore, stroke patients had more central apnea events (p = 0.007) and a trend for higher apnea/hypopnea number ratios (p = 0.091). The prevalence of OSA (apnea-hypopnea index ≥ 5) was 90% in acute stroke patients and 79% in transient ischemic attack patients., Conclusion: Stroke patients had different characteristics of respiratory events, i.e., their polygraphic phenotype of OSA differs compared to matched reference patients. The observed differences in polygraphic features might indicate that stroke and TIA patients suffer from OSA phenotype recently associated with increased cardiovascular mortality. Therefore, optimal diagnostics and treatment require routine OSA screening in patients with acute cerebrovascular disease, even without previous suspicion of OSA.

Purpose: Frequency of apnea and hypopnea events is used to estimate the severity of obstructive sleep apnea (OSA). However, comprehensive information on whether apneas and hypopneas cause an equal biological effect is not available. The purpose of the present work was to evaluate the effect of the breathing cessation event type (i.e., obstructive apnea or hypopnea) and duration on the severity of related SpO 2 desaturation events., Methods: Type 1 polysomnographies of 395 patients (220 males and 175 females) examined for suspected OSA were analyzed. Desaturation severity related to hypopnea and obstructive apnea events were compared and comparison was controlled for gender, sleep stage, sleeping position, age, and body mass index. Hypopneas and obstructive apneas were further divided into eight different durational categories and related desaturation event characteristics were compared between the categories., Results: SpO 2 desaturation events caused by obstructive apneas were statistically significantly (p ≤ 0.004) longer, greater in area, and deeper compared to the SpO 2 desaturations caused by hypopneas. The increase in the duration of hypopnea and obstructive apnea events led to increase in the duration and area of related SpO 2 desaturations. The increase in the obstructive apnea event duration also led to increase in the depth of related desaturation event., Conclusions: Obstructive apneas led to more severe SpO 2 desaturation compared to hypopneas. Increased event duration led to increase in the severity of the related SpO 2 desaturation. In addition to considering event duration, obstructive apneas should have more weight than hypopneas when estimating severity of OSA and associated long-term cardiovascular risk.