Search Results (13)

To address issues such as attitude instability, insufficient adaptability, and poor operational quality of precision operation equipment caused by complex terrain conditions in hilly orchards, this study designed an electric carrier Self-Leveling Platform based on the 3-RRS parallel configuration. Focusing on the stability requirements of the operation plane, an automatic leveling control strategy was proposed with the constant center height of the moving platform as an additional constraint condition. Based on the inverse kinematics solution of the 3-RRS Parallel Mechanism, the analytical mapping relationship between the fuselage attitude and the compensation angle of the leveling leg crank was derived, and based on this, the working space of the Self-Leveling Platform and the maximum compensation angles of the moving platform in the pitch and roll directions were calculated. Key structural parameters were optimized using a multi-objective genetic algorithm, followed by the completion of a 3D model design and modal simulation analysis to verify the effectiveness of the structural design. Finally, leveling performance tests were conducted on a prototype. The results showed that the platform can achieve omnidirectional automatic leveling, with a maximum leveling time of 1.593 s and a maximum steady-state error of 0.62° under typical slope and load conditions. Analysis of variance results further indicated that there are significant differences in the leveling performance of the 3-RRS parallel configuration of the Self-Leveling Platform in the pitch and roll directions, demonstrating anisotropic characteristics. This study provides an effective solution for attitude stability control of orchard operation equipment in hilly areas and offers theoretical reference and technical support for the application of the 3-RRS parallel configuration in the agricultural equipment field. Full article

Due to the evident increase in tick-borne diseases worldwide, it is necessary to constantly update information on the distribution and zoonotic potential of hard ticks. We studied diversity, population structure, and seasonal dynamics of hard tick fauna, faunal similarity and the climate impact on tick occurrence in natural habitats (NHs) (forest communities) and anthropogenic habitats (AHs) (orchards, grasslands, degraded forests) in eastern and central parts of Continental Croatia. Host-seeking hard ticks were sampled by the flag-dragging method in lowland AHs (Bansko Hill (BH); 2023–2024 yr.) and in mountainous NHs (Medvednica Mountain (MM); 2019–2021, 2024 yr.). Overall, 2726 specimens belonging to eight hard tick species (Ixodes ricinus, I. frontalis, I. hexagonus, I. kaiseri, Haemaphysalis inermis, H. concinna, Dermacentor marginatus, D. reticulatus) were identified in AHs, while in NHs 1543 hard ticks, belonging to three species (I. ricinus, I. frontalis, D. reticulatus), were collected. The most abundant species in both habitat types (47.83% in AHs, 99.80% in NHs) was I. ricinus, showing unimodal seasonal activity within studied NHs and bimodal activity at AHs. Comparison of hard tick fauna in different habitats using the Sørenson index on BH and MM showed a high percentage of similarity (50.0–88.8). At AHs, a significant (p < 0.05) negative correlation was determined between the abundance (N) and the mean monthly air temperatures (°C) for H. inermis (r = −0.5931; p = 0.0421) and D. reticulatus (r = −0.6289; p = 0.0285), while their numbers positively correlated (r = 0.5551; p = −0.2667; r = 0.4430; p = 0.1492) with air humidity (%). In contrast, the number of sampled host-seeking I. ricinus ticks at natural forest habitats on MM was positively associated with air temperature and negatively with air humidity at elevations from 200 to 1000 m a.s.l. (r = −0.7684; p = 0.0259; at 200 m a.s.l.). Collected specimens of I. frontalis mark the first record for Osijek–Baranja County, while the sampled D. reticulatus on MM represents the first catch at 1000 m a.s.l. in Croatia. This new data on the distribution and seasonality of medically important hard tick species in Continental Croatia contributes to identifying tick-risk foci and high-risk periods. Full article

Food grain production has multiplied over the last two decades in India, but natural resources are overexploited in modern farming. Farmers, especially those with small and marginal holdings, are suffering losses more often than not, the cost of production is increasing year after year, and profits are not up to the necessary levels. To address such challenges, there has been a broad recognition of the importance of employing farming system approaches in research. The cultivation of cropping systems with orchard crops and livestock components can play a significant role in the optimal utilization of resources, enhancing energy use efficiency as well as the eco-efficiency index, and reducing carbon footprints. This study was carried out to create a suitable IFS model with high economic and energy efficiency for small-holder farmers in India’s southern plateau and hills with a negligible impact on the environment. The following were the seven models: M₁: Rice − Groundnut; M₂: Rice − Groundnut, Pigeonpea + Sweetcorn (1:3) − Bajra, Bt cotton + Greengram (1:2) − Maize; M₃: Rice − Groundnut, Pigeonpea + Sweetcorn (1:3) − Bajra, Pigeonpea + Maize (1:3) − Sunhemp; Napier grass, Sheep (5 + 1); M₄: Rice − Groundnut, Pigeonpea + Sweetcorn (1:3) − Bajra, Bt cotton + Greengram (1:2) − Maize, Pigeonpea + Maize (1:3) − Sunhemp, Poultry unit; M₅: Guava, Hedge Lucerne, Napier grass, Bt cotton + Greengram (1:2) − Maize, Sheep (5 + 1); M₆: Guava, Bt cotton + Greengram (1:2) − Maize, Rice − Groundnut, Poultry; M₇: Rice − Groundnut, Pigeonpea + Sweetcorn (1:3) − Bajra, Pigeonpea + Maize (1:3) − Sunhemp; Napier grass, Hedge lucerne, Poultry (100), Sheep (5 + 1). Model M₁ was used to represent the local region, and the other models were compared in terms of economics, energetics, greenhouse gas emissions, and employment creation. The M₇ and M₃ models, according to the results, have higher economic efficiency (₹342.3 day⁻¹, ₹263.7 day⁻¹), increase output energy (228,529 and 183,231 MJ) net energy (258,184 and 198,920 MJ), produce net negative emissions (−2842 and −2399 kg CO₂ eq.), and create jobs year-round (112.5 and 110.5 man days year⁻¹), respectively. This is primarily because they have multiple highly efficient components that make them viable for Telangana’s small and marginal farmers. Full article

In recent years, pistachio (Pistacia vera L.) cultivation is undergoing a great expansion in Spain, which is promising for regions where water and winter chilling are not limiting. Many areas of Castilla y León (Spain) provide suitable conditions for pistachio production, but heat requirement could be a limiting factor. The aims of this study were (i) to investigate the status of pistachios in Castilla y León and the relationships between phenology and agroclimatic conditions and (ii) to assess the performance of three pistachio cultivars (‘Kerman’, ‘Lost Hills’, and ‘Golden Hills’) in a plantation within this region. This work describes the phenological and productive behavior of three pistachio varieties in seven orchards over three years. The chilling requirements were exceeded, and heat accumulation was sufficient to complete the cycle in all seasons. Bloom and harvest occurred later in ‘Kerman’ than in ‘Golden Hills’ and ‘Lost Hills’. In general, ‘Kerman’ had higher nut yield than the other two cultivars but also had more non-split and blank nuts, aspects that should be considered for future plantations. Despite the interannual variability in yield, a trend to increase the production with water received was observed, but this also affected the quality and modified the splitting percentage. Full article

To create a digital unmanned orchard with automation of “picking, load and transportation” in the hills and mountains, it is vital to determine a cargo-carrying situation and monitor the real-time transport conditions. In this paper, a cargo-carrying analysis system based on RGB-D data was developed, taking citrus transportation as the scenario. First, the improved YOLOv7-tiny object detection algorithm was used to classify and obtain 2D coordinate information on the carried cargo, and a region of interest (ROI) was obtained from the coordinate information for cargo height measurement. Second, 3D information was driven by 2D detection results using fewer computing resources. A depth map was used to calculate the height values in the ROI using a height measurement model based on spatial geometry, which obtained the load volume of the carried cargo. The experimental results showed that the improved YOLOv7 model had an accuracy of 89.8% and an average detection time of 63 ms for a single frame on the edge-computing device. Within a horizontal distance of 1.8 m from the depth camera, the error of the height measurement model was ±3 cm, and the total inference time of the overall method was 75 ms. The system lays a technical foundation for generating efficient operation paths and intelligently scheduling transport equipment, which promote the intelligent and sustainable development of mountainous agriculture. Full article

Researchers increasingly investigate ecosystem services to assess their role in supporting livelihoods, well-being and economic value in order to inform decision-making. Many studies have explored links between ecosystem services and community-based livelihoods, with a very narrow focus on the importance of land use to well-being. We evaluated the value of ecosystem services from various land uses supporting livelihoods and the overall well-being of local communities in the Chittagong Hill Tracts (CHT) of Bangladesh. By applying a participatory habitat valuation approach with the ethnic communities from eight villages, we explored their preferences for, and perceptions of, ecosystem services and their sources in a multi-functional landscape under different land use, i.e., forest, swidden and low-land agriculture, fruit orchard and water bodies, and three land ownership contexts (state, private and mixed ownership on forest lands). Our findings revealed that community land use preference for ecosystem services supports ten different well-being needs. Among others, forests were valued land used for two-thirds of well-being needs, including the provision of shelter, nutrition, primary health care, an adequate supply of potable water, a lower level of ecological stress (i.e., protection from associated landslide soil erosion), cultural and spiritual benefits and livestock foraging. People commonly valued the food, income and nutrition contributions of all land uses. However, different forest and land ownership contexts and rights within the landscape influence people’s preference for ecosystem services from land use in supporting their well-being. People with secure ownership (i.e., private and private-community) showed a broad and positive appreciation for ecosystem services to meet their well-being needs. Our study highlights that local and ethnic people’s land-use preferences and ownership contexts are critical factors in assessing well-being in the context of multifunctional landscapes. We recommend that ecosystem services be considered in future decision-making related to forest and land use to support human well-being. Full article

Neurodegenerative diseases are driven by several mechanisms such as inflammation, abnormal protein aggregation, excitotoxicity, mitochondrial dysfunction and oxidative stress. So far, no therapeutic strategies are available for neurodegenerative diseases and in recent years the research is focusing on bioactive molecules present in food. In particular, extra-virgin olive oil (EVOO) phenols have been associated to neuroprotection. In this study, we investigated the potential antioxidant and neuroprotective activity of two different EVOO extracts obtained from Quercetano cultivar trees grown in two different areas (plain and hill) of the Tuscany region (Italy). The different geographical origin of the orchards influenced phenol composition. Plain extract presented a higher content of phenyl ethyl alcohols, cinnammic acids, oleacein, oleocanthal and flavones; meanwhile, hill extract was richer in lignans. Hill extract was more effective in protecting differentiated SH-SY5Y cells from peroxide stress thanks to a marked upregulation of the antioxidant enzymes heme oxygenase 1, NADPH quinone oxidoreductase 1, thioredoxin Reductase 1 and glutathione reductase. Proteomic analysis revealed that hill extract plays a role in the regulation of proteins involved in neuronal plasticity and activation of neurotrophic factors such as BDNF. In conclusion, these data demonstrate that EVOOs can have important neuroprotective activities, but these effects are strictly related to their specific phenol composition. Full article

The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified. Full article

Chestnut forests represent an important environmental and landscape element in Europe, especially in the hill regions of southern Europe. In Italy, the total surface amount of chestnut forests is slightly expanded with 788,408 hectares, but orchards show a dramatic reduction (147,586 hectares or 20% of the total) and timber-producing stands a comparable expansion (605,888 hectares or 80%). The coppice management actually applied is considered one of the oldest forms of sustainable forest management. Over the years, coppice highlighted its versatility, resilience and multifunctionality. In this management system, in consideration of the “frequent” cutting cycles, special attention must be paid to forestry operations, because tree damage and soil compaction can trigger fungal disease and soil erosion. Frequent and repeated machine traffic increases the risk for soil degradation derived from compaction, topsoil removal and general disturbance. This study covered different forest areas and mechanization levels, in order to evaluate if the extent, type and severity of soil disturbance changed with site characteristics and logging technique. Furthermore, the study sought to obtain a better knowledge about the recovery time required for restoring the original soil properties after a disturbance has occurred. The findings showed that physical, chemical, and biological soil features were only partially disturbed by the coppicing and again that a high level of specialized mechanization does not generate heavier soil impact compared with the smaller and lighter machines deployed under the traditional and intermediate mechanization scenarios. Soil recovery in the impacted areas is already measurable one year after harvest and may be complete within the eight year—that is halfway through the standard rotation applied in the region to chestnut coppice. Full article

This study aims to monitor the flash flood response of Vidor/Wadore hill torrent in Pakistan by the integration of Personal Computer Storm Water Management Model PCSWMM (hydrologic) and HEC-RAS 5.x (hydraulic) models. The method leverages remote sensing and GIS derive estimates of measured and inferred parameters of Vidor rural catchment to quantify the flash flood events of the last four years: 2014–2017. The calibration of the PCSWMM is performed using the sensitivity-based radio tuning calibration (SRTC) tool. The Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and relative error (RE) values were found between 0.75–0.97, 0.94–0.98, and −0.22–−0.09 respectively. The statistical indicators prove the accuracy of PCSWMM for rural catchments. The runoff response of Vidor torrent is also analyzed for 0.5/12.7, 1.5/38.1, and 2.0/50.8-inch/mm rainfall hyetographs. The generated hydrographs are used to simulate 2D-module in HEC-RAS 5.x for floodplain demarcation in the piedmont area. The accuracy of the flood extent is analyzed using spatial overlay analogy in the ArcGIS environment by comparing simulated and historically available flood extents. The simulated flood extent shows 76% accuracy with historic flood extent. The impact of flash flood events shows wheat, maize, and fruit orchards are the most effected agriculture in piedmont area. The results revealed that the integration of hydrological, hydraulic, and geospatial modeling approaches can be used to model a full picture of catchment response during flash flood events. Full article

Upland agricultural expansion and intensification cause soil erosion, which has a negative impact on the environment and socioeconomic factors by degrading the quality of both nutrient-rich surface soil and water. The Haean catchment is a well-known upland agricultural area in South Korea, which generates a large amount of sediment from its cropland. The transportation of nutrient-rich sediment to the stream adversely affects the water quality of the Han River watershed, which supports over twenty million people. In this paper, we suggest a spatially explicit mitigation method to reduce the amount of sediment yield to the stream of the catchment by converting soil erosion hot spots into forest. To evaluate the effectiveness of this reconfiguration, we estimated the sediment redistribution rate and assessed the soil erosion risk in the Haean catchment using the daily based Morgan–Morgan–Finney (DMMF) model. We found that dry crop fields located in the steep hill-slope suffer from severe soil erosion, and the rice paddy, orchard, and urban area, which are located in a comparatively lower and flatter area, suffer less from erosion. Although located in the steep hill-slope, the forest exhibits high sediment trapping capabilities in this model. When the erosion-prone crop lands were managed by sequentially reconfiguring their land use and land cover (LULC) to the forest from the area with the most severe erosion to the area with the least severe erosion, the result showed a strong reduction in sediment yield flowing to the stream. A change of 3% of the catchment’s crop lands of the catchment into forest reduced the sediment yield entering into the stream by approximately 10% and a change of 10% of crop lands potentially resulted in a sediment yield reduction by approximately 50%. According to these results, identifying erosion hot spots and managing them by reconfiguring their LULC is effective in reducing terrestrial sediment yield entering into the stream. Full article

Pre-harvest yield estimation of mango fruit is important for the optimization of inputs and other resources on the farm. Current industry practice of visual counting the fruit on a small number of trees for yield forecasting can be highly inaccurate due to the spatial variability, especially if the trees selected do not represent the entire crop. Therefore, this study evaluated the potential of high resolution WorldView-3 (WV3) satellite imagery to estimate yield of mango by integrating both geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model. WV3 images were acquired in 2016–2017 and 2017–2018 growing seasons at the early fruit stage from three orchards in Acacia Hills region, Northern Territory, Australia. Stratified sampling technique (SST) was applied to select 18 trees from each orchard and subsequently ground truthed for yield (kg·tree⁻¹) and fruit number per tree. For each sampled tree, spectral reflectance data and tree crown area (TCA) was extracted from WV3 imagery. The TCA was identified as the most important predictor of both fruit yield (kg·tree⁻¹) and fruit number, followed by NDVI red-edge band when all trees from three orchards in two growing seasons were combined. The results of all sampled trees from three orchards in two growing seasons using ANN model produced a strong correlation (R² = 0.70 and 0.68 for total fruit yield (kg·tree⁻¹) and fruit number respectively), which suggest that the model can be obtained to predict yield on a regional level. On orchard level also the ANN model produced a high correlation when both growing seasons were combined. However, the model developed in one season could not be applied in another season due to the influence of seasonal variation and canopy condition. Using the relationship derived from the measured yield parameters against combined VIs and TCA data, the total fruit yield (t·ha⁻¹) and fruit number were estimated for each orchard, produced 7% under estimation to less than 1% over estimation. The accuracy of the findings showed the potential of WV3 imagery to better predict the yield parameters than the current practice across the mango industry as well as to quantify lost yield as a result of delayed harvest. Full article

Avocado (Persea americana) fruits are an important source of income and a nutritious food for small-scale growers and other stakeholders involved in farming along the Afrotropical highlands of Taita Hills and Mount Kilimanjaro in Kenya and Tanzania, respectively. Avocado fruits are infested by several insect pests, namely the Asian invasive fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), and the false codling moth, Thaumatotibia leucotreta Meyrick (Lepidoptera: Tortricidae). However, there is inadequate information on the distribution patterns of these pests in small-scale avocado cropping systems in the East African highlands. This study was initiated to generate a spatial distribution map of B. dorsalis and T. leucotreta in avocado orchards at Taita Hills and Mount Kilimanjaro in Kenya and Tanzania, respectively. The two pests were monitored by using their respective parapheromone lures for two years between August 2012 and July 2014. Fruit damage was assessed by computing the proportion of infested fruits for B. dorsalis, whereas the damage score was used for T. leucotreta. Our results indicated that the mean number of B. dorsalis per trap per day differed significantly across elevation, being highest in lowland zone for both Taita Hills (15.90) and Mount Kilimanjaro (24.45). Similarly, the percentage infestation of ground collected fruits by B. dorsalis varied with altitude, being lowest at highlands above 1500 m.a.s.l. (0.66% and 0.83% for Taita Hills and Mount Kilimanjaro, respectively). Conversely, the mean number of T. leucotreta did not vary with altitude in either study area. However, the damage score for T. leucotreta infestation was significantly lower in the highlands of both transects (7.0% and11.1% for Taita Hills and Mount Kilimanjaro, respectively). These findings describe spatial trends that are important in formulating strategies aimed at suppressing the populations of B. dorsalis and T. leucotreta in East African avocado cropping systems. Full article