An Overview of Software Sensor Applications in Biosystem Monitoring and Control.

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems-from cellular interactions to ecological dynamics-are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development.

The Nile Delta of Egypt is increasingly facing sustainability threats, due to a combination of nature- and human-induced changes in land cover and land use. In this paper, an analysis of big time series data from remotely sensed satellite images and the random forests classifier was undertaken to assess the spatial and temporal dynamics of urbanization and cropland in the Nile Delta between 2007 and 2017. Out of thirteen variables, five spectral indices were chosen to build 500 decision trees, with a resulting overall accuracy average of 91.9 ± 1.5%. The results revealed that the urban extent in the Nile Delta has increased, between 2007 and 2017, by 592.4 km2 (1.92%). Particularly, the results indicated that the years 2011 and 2012, which coincided the 2011 political uprising in Egypt, so-called the Arab Spring, were associated with significant land-use changes in the Nile Delta, both in rate and scale. As a result, the cropland area in the region decreased between 2010 and 2011 by 1.63% (502.21 km2). Moreover, the results showed that during the period 2012-2017, the mean annual urbanization rate in the region stood at 60 km2/year. In contrast, croplands decreased during the same period at an average annual rate of 2 km2/year. At the governorates' level, the results suggested that top agricultural producing governorates in the Nile Delta, such as Elmonoufia, Elkalubia, Elbouhyra, and Elghrbia, witnessed the highest rates of decrease in cropland areas during the period 2012-2017. Over the same period, urban areas increased the most in Elkalubia, Domiate, and Elmonoufia by 1.98%, 1.72%, and 1.34%, respectively. The f indings from this analysis are discussed along with their implications for sustainable land-use and urban planning policies.

A spatial pattern analysis of the halophytic species distribution in an arid coastal environment.

Obtaining information about the spatial distribution of desert plants is considered as a serious challenge for ecologists and environmental modeling due to the required intensive field work and infrastructures in harsh and remote arid environments. A new method was applied for assessing the spatial distribution of the halophytic species (HS) in an arid coastal environment. This method was based on the object-based image analysis for a high-resolution Google Earth satellite image. The integration of the image processing techniques and field work provided accurate information about the spatial distribution of HS. The extracted objects were based on assumptions that explained the plant-pixel relationship. Three different types of digital image processing techniques were implemented and validated to obtain an accurate HS spatial distribution. A total of 2703 individuals of the HS community were found in the case study, and approximately 82% were located above an elevation of 2 m. The micro-topography exhibited a significant negative relationship with pH and EC (r = -0.79 and -0.81, respectively, p < 0.001). The spatial structure was modeled using stochastic point processes, in particular a hybrid family of Gibbs processes. A new model is proposed that uses a hard-core structure at very short distances, together with a cluster structure in short-to-medium distances and a Poisson structure for larger distances. This model was found to fit the data perfectly well.

Effectiveness of Chinese pine (Pinus tabulaeformis) plantation at reducing runoff and erosion rates in Anjiagou Watershed in Semi-arid Region of Gansu, China.

China's Loess Plateau regions have experienced severe soil erosion for many decades due to fragmented landscapes, steep slopes, and concentrated rainfall storm-events. Restoring sub-optimal or marginal farming fields, mostly on steep, hilly terrain, to plantation forests has been a long-standing strategic policy in China aimed at rehabilitating degraded environments and reducing soil and water erosion. While there are numerous studies that have focused on the effects of forests at controlling soil erosion at relatively short time scales, few have addressed longer-term effects of plantation forests on reducing runoff and the mechanisms that inhibit erosion. Chinese pine (Pinus tabulaeformis) has been widely planted in abandoned or reclaimed lands that were formerly farmed in Northwest China; however, there is limited knowledge about the effectiveness of the tree species at reducing soil and water erosion. In this study, we examined reduction rates of runoff and erosion by Chinese pine plantation in comparison with agricultural land as a control (i.e., wheat, a dominant agricultural commodity in the region), based on long-term monitoring of modified standard erosion plots with slopes of 10°, 15°, and 20°. Results showed that as the slope of the land increased, rates of erosion increased for both plantation and agricultural land use. However, the runoff and soil erosion rates under Chinese pine plantation forest were about 11% and 60% lower, respectively, than those under agricultural land use of the same slope. Scaling with the slope, the highest reduced runoff and erosion rates by Chinese pine forest were 17% and 72%, respectively, on 20° slope. Also, it was found that runoff rates from the forested land were positively related to erosive rainfall (i.e., rainfall when runoff generated), and varied with forest canopy coverage. The rates of runoff and erosion can be well model led with multiple regression models. Taken together, this study provides insight into the importance and potential of Chinese pine plantations in the conservation of soil and water in China's Loess Plateau.

Analysis and estimation of nonpoint source pollution under different land use in Anjiagou watershed, Gansu, China.

Agriculture-related nonpoint source pollution has been a worldwide issue in the past few decades. Estimating pollutant sources at the basin scale remains a challenge due to the complexity of pollutant transport mechanisms affected by land use, variable climatic condition, geological formation, and lack of long-term observation data. This study was based on the long-term plot observational data of surface runoff and water quality and used principal component analysis and other statistical analyses to analyze the variation of water quality affected by different land uses (cropland, tree forest, shrub, grassland, and wildland). The mean concentration method with the local calibrated/verified SWAT (soil and water assessment tool) model was used to quantify the load of nonpoint source (NPS) pollutants on slope areas under different land uses in the Anjiagou Watershed. Our research results determined permanganate index (CODMn), ammonia nitrogen (NH3-N), total nitrogen (TN), fluoride (F-), nitrite nitrogen (NO2-N), total phosphorus (TP), and hexavalent chromium (Cr6+) as the significant pollutants while 5-day biochemical oxygen demand (BOD5) was identified to be below the water quality standards of Grade V (water for agricultural and general landscape use) in the studied watershed. Pollutants were discharged through either hillslope at a total rate of 2.4 kg ha-1, accounting for 67.6% of the total, or through waterway channels (32.4%). The pollutant concentrations were from 23.5 mg L-1 to 37.4 mg L-1, varying with pollutants and land uses. All examined water quality indicators exceeded the minimum safety standards defined by the regulations of the Gansu provincial government by averaging 3.5 times higher than the safety threshold. The pollutants from hillslopes exceeded water quality standards by a factor of 3.4-4.4 times compared with from the waterway channel by 1.9. Implementing soil and water conservation measures can mitigate pollutants to some extent, particularly during the process of highland runoff converging into waterways. At the watershed level, between 33 and 38% of the runoff and pollutants were discharged from croplands, between 59 and 66% from forest land, < 2% from grassland, and 1% from wildland. This study also demonstrates a simple but novel method to estimate NPS pollutants using long-term plot observations in conjunction with SWAT models, which can be used in other watersheds with similar conditions.