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Cover crops between rows in orchards can improve the development of soil resources and increase agricultural productivity. However, there have been few reports of cover crops that can act as a "green manure" in apple orchards across arid and semiarid zones. This study investigated the effects of planting interrow vegetation on soil properties and apple tree performance during a 32-month experiment. There were six treatments: clean cultivation as a control; natural grass planting; planting with ryegrass; planting with alfalfa; planting with tall fescue; and planting with villous wild pea cover crops. The treatments primarily affected the 0- to 20-cm surface soil layer. Soil carbon, nitrogen, and enzyme levels initially decreased (during the first 12-24 months); then, they increased (24-32 months). The cover crops significantly increased nutrient contents (soluble organic carbon, microbial carbon and nitrogen, alkaline dissolved nitrogen, nitrate nitrogen, and ammonium nitrogen) in the 0- to 20-cm soil layer by more than 19.6% and increased the related enzyme activities by more than 25.2%. The alfalfa and wild pea alleys had a stronger effect on the soil environment than the control, natural grass, ryegrass, and tall fescue alley treatments; however, after 32 months, the alfalfa treatment inhibited fruit tree growth and development. This was unexpected because alfalfa seemed to have a positive effect on soil fertility characteristics. Under local ecological conditions, villous wild pea had the greatest effect on apple orchard productivity and significantly increased short branching by 15.9%, fruit weight per fruit by 12.6%, yield per plant by 8.6%, and soluble sugar content by 10.5% compared with clean cultivation. The correlation analysis showed that there were significant or highly significant positive correlations between fruit tree performance and soil carbon, nitrogen, and enzyme activity levels as the soil layer depth increased. Therefore, under local ecological conditions, cover crops have a greater effect on orchard surface soil fertility than on deeper soils, and intercropping with villous wild pea potentially produces the greatest improvement in apple orchard productivity. [ABSTRACT FROM AUTHOR]

A new controllable rotary bending cracking cropping method, based on V-notch stress concentration, is proposed to address the drawbacks of the metal bar separation process, such as poor cross-section quality and low material utilization. The controlled rotary bending cracking cropping experimental device is developed to implement this method. Subsequently, a mechanical model for the precision cropping process is established using material strength theory, and a load control strategy based on fracture mechanics theory is applied to predict cropping efficiency. Experimental verification is then conducted, with results indicating that increasing notch depth improves cropping efficiency and cross-section quality. Additionally, maintaining constant stress intensity factor amplitude (ΔK = 0.4Kc) and notch depth of 5 mm yields optimal cropping effects. The precision cropping process leverages the fatigue crack propagation mechanism to explain the cropping process, offering theoretical guidance for selecting appropriate parameters in subsequent precision cropping processes.

Sports multimedia is among the most prominent types of content distributed across social media today, and the retargeting of videos for diverse aspect ratios is essential for a suitable representation on different social media platforms. In this respect, ice hockey is quite challenging due to its agile movement pattern and speed, and because the main reference point (puck) is very small. In this paper, we introduce a novel pipeline for intelligent video cropping tailored for ice hockey. Our main goal is to identify regions of interest in video frames by detecting and tracking the hockey puck using state-of-the-art AI models. Our pipeline employs scene detection, object detection, outlier detection, and smoothing as key features. Our proposed pipeline called SmartCrop-H is not only highly efficient and configurable with respect to target aspect ratios, but also addresses the automation needs in this domain. Our comprehensive evaluation, comprising objective and subjective measures, shows the overall efficiency of the entire pipeline, including assessments of both the individual components and the end-to-end system performance. We also demonstrate the practical applicability of SmartCrop-H with a user study, which indicates that our framework performs on par with, or even surpasses, professional tools in terms of output quality.

The frequent occurrence of extreme weather in recent years poses a significant threat to food production. Ensuring food production and rationalizing the use of agricultural resources require addressing the problem of the improper application of chemical fertilizers. Several effective measures have been implemented in China to reduce agricultural non-point source pollution. Among them, the reduction of excessive nitrogen fertilizer application proves to be the most effective approach in controlling surface pollution from cultivation. Currently, it is crucial to clarify and quantify crop nutrient fertilizer requirements while evaluating the potential for reducing nitrogen fertilizer usage in China. Nitrogen requirements for major crops grown in China were assessed based on the theory of crop nutrient balance, assuming constant grain production as a guarantee. In this paper, we analyze the potential for nitrogen reduction through short-term, medium-term, and long-term scenario predictions. The results show that in the next 3 years, China has a reduction potential of 34.98%, but this potential is not sustainable. Over the next 10 years, there is a reduction potential of 15.04%, with most provinces experiencing a balanced state of soil nitrogen cycling. Hainan, Beijing, Shaanxi, and Fujian have higher reduction potential, with possible reductions of 69.95%, 64.14%, 60.72%, and 54.10%, respectively. However, there are still provinces in China where nitrogen fertilizer is insufficient, leading to soil nitrogen consumption. Specifically, Heilongjiang, Jiangxi, and Shandong Provinces need to increase their nitrogen fertilizer applications by 87.00%, 35.97%, and 8.31%, respectively. The long-term scenario analysis over the next 30 years shows a reduction potential of 40.96%. Among the regions analyzed, Hainan, Beijing, Shaanxi, Fujian, and Ningxia have higher nitrogen fertilizer reduction potentials, with values of 78.97%, 78.48%, 74.25%, 67.87%, and 67.72%, respectively. However, Heilongjiang Province still needs to increase nitrogen fertilizer application by 44.20% to address soil nitrogen depletion. Conversely, Tibet and Qinghai, with high organic fertilizer yields, lower chemical fertilizer usage, and low nitrogen loss coefficients, are well-suited for organic agriculture development. For areas with high organic fertilizers usage and a risk of fertilizer loss, we recommend implementing the organic-inorganic mixed fertilization planting mode. [ABSTRACT FROM AUTHOR]

The contribution of wild pollinators and input-based management practices during production of underutilized crop such as okra are pivotal for enhancing sustainable food production. Information on the beneficial role of insect pollination on okra and soil management practices is scanty, details on their specific response at each location are required to evaluate the potential of these sustainable farming practices. We examined the effects of insect pollination (pollinator-exclusive approach) and soil fertilization (fertilizer application) on okra production, using a factorial experiment arranged in a split-plot design with four replications. The complementary benefits of insect pollination and soil fertilization were evident in yield parameters such as seed weight, fruit nodes and the number of pod/plants. A substantial increase in yield began in trials with 50 % pollination rate under minimal soil fertilization. The increase in okra seed weight was (42 %) and (36 %) higher in optimal fertilized plants with 100 % and 50 % pollination rates than in non-pollinated and unfertilized plants. The similarities in yield observed from optimal fertilized soil with 50 % and 100 % pollination rates, imply that optimal soil fertilization can alleviate the yield reduction in okra due to moderate decline in pollinators exposure. Among the important okra pollinators were bees, flies, wasps, beetles and lepidopterans. Bees provided most pollination services, while other wild pollinator groups made less frequent but still give significant contributions. Thus, yield maximization requires an improvement in both agroecosystem services and soil management practices. Adequate pollinator visitation improves okra productivity in high insect-pollinated cropping systems.

The present investigation was carried out to study the differential responses to assimilated nutrient management in the cabbage–capsicum–radish cropping system to develop an integrated plant nutrient supply. The experimental trial was laid out in a randomized complete block design (RCBD) with three replicates and included assimilations of 15 various combinations: T1—recommended dose of fertilizers (RDFs) + farmyard manure (FYM) (Control); T2—nitrogen and phosphorus (NP) + vermicompost (VC) (75% + 2.5 t/ha); T3—NP + VC (50% + 2.5 t/ha); T4—NP + enriched compost (EC) (75% + 2.5 t/ha); T5—NP + EC (50% + 2.5 t/ha); T6—NP + plant-growth-promoting rhizobacteria (PGPR) (75% + 5 kg/ha); T7—NP + PGPR (50% + 5 kg/ha); T8—NP + VC + PGPR (75% + 2.5 t/ha + 5 kg/ha); T9—NP + VC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T10—NP + EC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T11—NP + EC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T12—NP + VC and EC (75% + 2.5 t/ha and 2.5 t/ha); T13—NP + VC and EC (50% + 2.5 t/ha and 2.5 t/ha); T14—NP + VC and EC + PGPR (75% + 2.5 t/ha and 2.5 t/ha + 5 kg/ha); T15—NP + VC and EC + PGPR (50% + 2.5 t/ha and 2.5 t/ha + 5 kg/ha) for two consecutive years. Seedlings of the cabbage cultivar were transplanted with a spacing of 45 × 30 cm, whereas the capsicum seedlings were transplanted with a spacing of 60 × 45 cm. The radish cultivar was sown directly in the field with a spacing of 30 × 7.5 cm. The yield and growth attributes of all three crops were notably impacted by the INM modules. The utilization of a combination of 75% of the recommended quantity of NP + VC and EC, along with PGPR, at a rate of 2.5 t/ha, during cabbage cultivation, led to a noteworthy rise in plant height, equatorial diameter, gross head weight, net head weight, and ultimately, the maximum head yield, as per the statistical analysis. In the case of capsicum, the treatment (T14) module demonstrated superior performance in terms of the major yielding components, namely, fruit size, fruit weight, and number of fruits per plant, resulting in the highest yield compared to the other modules, including RDFs. The cabbage exhibited high protein content and inorganic modules in terms of quality traits, whereas ascorbic acid and total soluble solids (TSSs) levels were high due to strong organic support across all three crops. Thus, it can be inferred that the integrated combination of 75% NP, VC, and EC at 2.5 t/ha, and PGPR, along with the basic application of the full recommended potash and farmyard manure (FYM), led to a reduction of 25% in fertilizers (NP), improved growth and yield, and higher annual net returns. Thus, this incorporation can be suggested as an economically efficient strategy for consistently attaining increased productivity with enhanced excellence. [ABSTRACT FROM AUTHOR]

In a non-professional environment, multi-camera recordings of theater performances or other stage shows are difficult to realize, because amateurs are usually untrained in camera work and in using a vision mixing desk that mixes multiple cameras. This can be remedied by a production process with high-resolution cameras where recordings of image sections from long shots or medium-long shots are manually or automatically cropped in post-production. For this purpose, Gandhi et al. presented a single-camera system (referred to as Gandhi Recording System in the paper) that obtains close-ups from a high-resolution recording from the central perspective. The proposed system in this paper referred to as “Proposed Recording System” extends the method to four perspectives based on a Reference Recording System from professional TV theater recordings from the Ohnsorg Theater. Rules for camera selection, image cropping, and montage are derived from the Reference Recording System in this paper. For this purpose, body and pose recognition software is used and the stage action is reconstructed from the recordings into the stage set. Speakers are recognized by detecting lip movements and speaker changes are identified using audio diarization software. The Proposed Recording System proposed in this paper is practically instantiated on a school theater recording made by laymen using four 4K cameras. An automatic editing script is generated that outputs a montage of a scene. The principles can also be adapted for other recording situations with an audience, such as lectures, interviews, discussions, talk shows, gala events, award ceremonies, and the like. More than 70 % of test persons confirm in an online study the added value of the perspective diversity of four cameras of the Proposed Recording System versus the single-camera method of Gandhi et al.

In the article, information on the use of Amudarya waters, the use of the nature along the Amudarya (thickets along the river and their animals), the ecology of the areas crossed by the Amudarya, the prevention of erosion of the river banks, and the transportation of modern boats are analyzed through sources.

This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I–F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117–0.215 and 0.701–0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme. [ABSTRACT FROM AUTHOR]

Agriculture is the bravado of most developing countries like Nigeria; thus, it is the key point for the existence of those living in the country. This is study is aimed at the effect of long-term tillage practice and cropping on physicochemical properties of soils in Southern guinea savannah area of Nigeria. Soil samples were collected from a continuously cropped rice field using the random uniform grid method at depths between 0 - 15 cm, 15 – 25 cm, 25 - 50 cm and 50 – 75 cm. Samples were collected in five replicates, mixed and homogenized. Soil samples collected at the 0 - 15 cm, 25 - 50 cm depth were Sandy clay loam while those of 15 - 25 cm and 50 - 75 cm depths are sandy clay and sandy loam. The soil hydraulic conductivity for the zone of 25 to 50 cm had values ranging between 0.23 and 1.51 cm/hr. while the fourth zone considered had values ranging between 0.49 and 0.77 cm/hr. It was concluded that the farmers should conduct physical and chemical analysis of the soils they wish to carryout farming activities to decide the type of fertilizer to be used.

The provision of water for plants is a major component supporting the success of planting on dry land. One source of water that can be utilized is groundwater. This study aims to determine the use of groundwater in supporting the cultivation of groundnut, corn and soybeans on dry land during dry season. The study used plots of farmers' land to grow groundnut, corn and soybeans complete with technology packages for each commodity. The application of technology includes cultivating groundnut by farmers and cultivating groundnut, corn and soybeans according to the recommended technology components. Harvest data were analyzed descriptively. Economic analysis is used to calculate the profitability and feasibility of farming. The results showed that corn plants with technology recommendations gave the highest yield, dry seeds of 5.50 tons ha-1, dry biomass of 18.63 tons ha-1, with carbon absorption in seeds of 2.62 tons ha-1 and biomass of 8.92 tons ha-1. The profit from corn cultivation was 22,090,000 IDR ha-1 with a benefit-cost (B/C) of 1.35, groundnut 20,985,000 IDR, with a higher B/C of 1.50. Soybean plants provide the lowest profit and B/C, amounting to 4,124,000 IDR and 0.26. Technologically and economically, the cultivation of groundnut, corn and soybeans with the support of groundwater irrigation is feasible to develop on dry land during the dry season. Socially, groundnut is more accepted by farmers.

This study was carried out to evaluate the technical efficiency of maize-based cropping systems in Ondo State, Nigeria. Primary data were used with the aid of a well-designed questionnaire. A multistage sampling procedure was used to randomly select 160 respondents. The data collected were analyzed using descriptive statistics, budgeting technique, and stochastic frontier production function model. The results of the budgetary technique revealed that the average gross margin per hectare was ₦17,715.03, ₦53,230.7 6, and ₦14,651 for sole maize cropping system, maize-cassava cropping system, and maize - yam cropping system, respectively. It was shown that household size and access to credit were the main determinants of the technical efficiency in sole maize cropping pattern, while access to credit and farming experience were the main determinants of technical efficiency in maize-cassava. In maize-yam, educational level and household size significantly affected technical efficiency. Farmers in the study area were producing at about 76% efficiency in sole maize cropping system, 89% efficiency in maize-cassava cropping system, 82% efficiency in maize-yam cropping system. The elasticity of variables inputs was 0.18 in sole maize cropping system, 0.61 in maize-cassava cropping system, and 0.33 in maize-yam cropping system. This indicates that all the maize-based cropping system had decreasing return to scale; therefore, the cropping systems fell within the rational stage of production surface. The implication is that maize-based farmers have not attained maximum efficiency in term of their production. Therefore, policy attention should be directed towards appropriate production technique that would improve productivity in the study area. [ABSTRACT FROM AUTHOR]

Bean maize intercropping is an old cultivation scheme practiced at small scale by traditional farmers in Romania. Literature highlights the benefits of intercropping and the constrains related extension of this practice on largest areas. Studies on interspecific interactions have paramount importance in view of development of feasible approaches of environmentally friendly cultivation practices and for genetic improvement specific for performance in intercropping. Five climbing bean varieties were evaluated in sole cropping and in intercropping with one type of sweet maize cultivar. Sowing was successively caried out in an interval of 30 days, bean following maize. The performance of bean genotypes in each cropping system was assessed by phenotyping, of different traits. The results show a significant bean genotype x cropping system interaction for flowering and for seed yield. The most competitive bean varieties were taller, more vigorous, and late to maturity. Diversification of cultural systems by implementing bean maize intercropping seems to be an effective strategy in pest management, and further research will provide a basis for incorporating practical pest control schemes into intercropping systems. [ABSTRACT FROM AUTHOR]

Our study aimed to assess the role of inoculation of faba bean/wheat intercrops with selected rhizobacterial consortia (composed of one rhizobium and two P solubilizing bacteria “PSB”) to alleviate the effects of combined water deficit and P limitation on faba bean/wheat intercropping vs. monocropping under greenhouse conditions. One Vicia faba L (Aguadulce) and one Triticum durum L. variety (Karim) were grown as a sole crop or were intercropped in pots containing a sterilized substrate (sand:peat 4:1 v/v) with either rock phosphate (RP) (unavailable P) or KH2PO4 in the nutrient solution (available P). Plant inoculation was performed using the rhizobacterial consortia C1 (Rhizobium laguerreae, Kocuria sp., and Pseudomonas sp.) and C2 (R. laguerreae, Rahnella sp., and Kocuria sp.). Two weeks after inoculation, the plants were subjected to water deficit with 40% substrate water holding capacity (WHC) vs. 80% WHC for the well-watered plants. The trial was assessed at the flowering stage, and the results showed that inoculation with both consortia (C1 and C2) improved faba bean biomass in terms of shoot, root, and nodules dry weight compared to inoculation with rhizobia alone. C2 improved these parameters by 19.03, 78.99, and 72.73%, respectively. The relative leaf water content decreased under combined stress, especially in response to C1 conferring significant improvement of this parameter in wheat intercrops. In faba bean under P limitation, inoculation with C2 increased stomatal conductance (gs), phosphatase, and phytase activity by 35.73, 166.94, and 26.16%, respectively, compared to plants inoculated with rhizobia alone. Furthermore, C2 also improved membrane stability under P deficit by 44.33 vs. 16.16% for C1 as compared to inoculation with rhizobia alone. In sole-cropped faba bean, inoculation with both consortia improved N accumulation compared to single inoculation with an increase of 70.75% under P limitation. Moreover, under combined stress, inoculation with C2 improved biomass and N content (112.98%) in intercropped wheat compared to the sole crop. Our findings revealed that consortium C2 might offer an agronomic advantage under water and P deficit and could serve as a useful inoculum for enhancing faba bean and wheat production in monocropping and intercropping systems.

Ongoing land clearing is a key driver of biodiversity loss and climate change. Effective action to halt land clearing and land degradation ultimately relies on understanding patterns of land capability for production uses, in particular agriculture, as a key driver of land use. Here we describe a national agricultural land capability map for Australia, based on harmonized state agricultural land capability datasets and modelled pastoral capability. State-level agricultural land capability datasets capture regional variations in crop selection and suitability. Hence, we reclassified these datasets to fit a nationally consistent land capability ranking scheme. For regions in which agricultural capability data was not available, we modelled agricultural and pastoral capability and mapped this to the same ranking scheme. The national land capability dataset fills an immediate knowledge need for Australia. This dataset has wide potential for utilization, such as for retrospective analysis of land use policies and prospective regional planning initiatives to ensure forward looking policies and land use plans optimize land allocation.

Secret image sharing (SIS) has excellent properties such as loss tolerance and relatively low computational complexity, providing a brand-new solution for image security protection. However, there has been little research on the robustness of SIS systems, such as how to resist cropping or malicious tampering in shares. Existing related schemes generally focus on grayscale images and suffer from issues such as lossy recovery, weak robustness, and serious pixel expansion, which are challenging to meet the requirements of high-quality sensitive image applications. In this regard, a robust SIS scheme for color images is proposed, which can resist large-scale cropping and malicious tampering in shares. According to the idea of "breaking up the whole cropped area into parts, repairing the shares independently", the proposed scheme can realize lossless recovery of secret images through organic fusion of secret sharing, error-correcting code, and pixel re-arrangement techniques. Even if all the shares are cropped by 25%, it can still achieve lossless recovery regardless of whether the cropping positions intersect or overlap. It can also resist various malicious tampering (such as marking, defacing, and copy-move forgery) as well as image noise. Moreover, it avoids pixel expansion and requires no auxiliary encryption or preprocessing. Theoretical analysis and experimental results demonstrate that the proposed scheme is superior to existing schemes in terms of robustness and comprehensive performance, and is expected to promote the practical application of SIS. [ABSTRACT FROM AUTHOR]

A new controllable rotary bending cracking cropping method, based on V-notch stress concentration, is proposed to address the drawbacks of the metal bar separation process, such as poor cross-section quality and low material utilization. The controlled rotary bending cracking cropping experimental device is developed to implement this method. Subsequently, a mechanical model for the precision cropping process is established using material strength theory, and a load control strategy based on fracture mechanics theory is applied to predict cropping efficiency. Experimental verification is then conducted, with results indicating that increasing notch depth improves cropping efficiency and cross-section quality. Additionally, maintaining constant stress intensity factor amplitude (Δ K = 0.4 K c) and notch depth of 5 mm yields optimal cropping effects. The precision cropping process leverages the fatigue crack propagation mechanism to explain the cropping process, offering theoretical guidance for selecting appropriate parameters in subsequent precision cropping processes. [ABSTRACT FROM AUTHOR]

The present investigation was carried out to study the differential responses to assimilated nutrient management in the cabbage–capsicum–radish cropping system to develop an integrated plant nutrient supply. The experimental trial was laid out in a randomized complete block design (RCBD) with three replicates and included assimilations of 15 various combinations: T1—recommended dose of fertilizers (RDFs) + farmyard manure (FYM) (Control); T2—nitrogen and phosphorus (NP) + vermicompost (VC) (75% + 2.5 t/ha); T3—NP + VC (50% + 2.5 t/ha); T4—NP + enriched compost (EC) (75% + 2.5 t/ha); T5—NP + EC (50% + 2.5 t/ha); T6—NP + plant-growth-promoting rhizobacteria (PGPR) (75% + 5 kg/ha); T7—NP + PGPR (50% + 5 kg/ha); T8—NP + VC + PGPR (75% + 2.5 t/ha + 5 kg/ha); T9—NP + VC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T10—NP + EC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T11—NP + EC + PGPR (50% + 2.5 t/ha + 5 kg/ha); T12—NP + VC and EC (75% + 2.5 t/ha and 2.5 t/ha); T13—NP + VC and EC (50% + 2.5 t/ha and 2.5 t/ha); T14—NP + VC and EC + PGPR (75% + 2.5 t/ha and 2.5 t/ha + 5 kg/ha); T15—NP + VC and EC + PGPR (50% + 2.5 t/ha and 2.5 t/ha + 5 kg/ha) for two consecutive years. Seedlings of the cabbage cultivar were transplanted with a spacing of 45 × 30 cm, whereas the capsicum seedlings were transplanted with a spacing of 60 × 45 cm. The radish cultivar was sown directly in the field with a spacing of 30 × 7.5 cm. The yield and growth attributes of all three crops were notably impacted by the INM modules. The utilization of a combination of 75% of the recommended quantity of NP + VC and EC, along with PGPR, at a rate of 2.5 t/ha, during cabbage cultivation, led to a noteworthy rise in plant height, equatorial diameter, gross head weight, net head weight, and ultimately, the maximum head yield, as per the statistical analysis. In the case of capsicum, the treatment (T14) module demonstrated superior performance in terms of the major yielding components, namely, fruit size, fruit weight, and number of fruits per plant, resulting in the highest yield compared to the other modules, including RDFs. The cabbage exhibited high protein content and inorganic modules in terms of quality traits, whereas ascorbic acid and total soluble solids (TSSs) levels were high due to strong organic support across all three crops. Thus, it can be inferred that the integrated combination of 75% NP, VC, and EC at 2.5 t/ha, and PGPR, along with the basic application of the full recommended potash and farmyard manure (FYM), led to a reduction of 25% in fertilizers (NP), improved growth and yield, and higher annual net returns. Thus, this incorporation can be suggested as an economically efficient strategy for consistently attaining increased productivity with enhanced excellence.

Purpose – Farmers' adaptation strategies in agricultural production are required to minimise the negative impact of climate change on a nation's food production in developing countries. Based on the panel data of the provincial level in China from 2000 to 2017, this study aims to analyse the changing climate over recent years and farmers' adaptation strategy in terms of cropping in agricultural production. Design/methodology/approach – This study uses Simpson's diversity index (SDI) to measure the degree of crop diversity planted by farmers and evaluate the influence of climate change on farmers' cropping strategy using the fixed-effect model. Further, the authors estimate the impact of farmers' cropping strategy on their economic performances in two aspects including yields and technical efficiency of crops. Findings – The empirical results show that the overall climate appears a warming trend. Different from farmers in some other countries, Chinese farmers tend to adopt a more specialised cropping strategy which can significantly improve the technical efficiency and yields of crops in agriculture. In addition, as a moderating role, the specialised cropping can help farmers to alleviate the negative impact of climate change on technical efficiency of their crops. Originality/value – First, previous studies showed that the changing climate influenced farmers' adaptation strategies, while most studies focussed on multiple adaptation strategies from the farm-level perspective rather than cropping strategy from the nation-level perspective. Second, the present study investigates how the cropping strategy affects the economic performance (in terms of the technical efficiency and crop yields) of agricultural production. Third, the stochastic frontier analysis method is used to estimate the technical efficiency. Fourth, this study explores the moderating effect between farmers' cropping strategy and technical efficiency by introducing an interaction item of SDI and accumulated temperature.

The fragile balance in the world's carbon equilibrium through the discovery of cheap carbon-based fuels in the nineteenth century has led to mass industrialisation and an explosion in the world human population, including that of Pakistan. Farmers worldwide will need to adapt their production systems to accommodate global warming and increased climate extremes resulting from these man-made environmental changes. The focus will need to be on smallholder farmers who generate 53% of the world's food but who are least equipped to accommodate climate change. The most major limitation will be fresh water supply, no more exemplified than in Pakistan as Himalayan snowfall decreases and peak snow melt comes earlier in spring, limiting irrigation water for summer C4 crops such as corn, millet, sorghum and sugarcane. These are destined to replace the traditional C3 crops of wheat and rice, which will not be as suited to climate change conditions resulting from a projected mean 2 °C rise in ambient temperature. Smallholder farmers will need to access superior-quality seed for crop cultivars for both human food and animal forage bred to withstand climatic change. Quantitative genetic selection programs for tropically adapted livestock must be implemented with a major focus on Pakistan's Nili Ravi and Kundhi buffalo, together with Sahiwal cattle servicing the milk consumption needs of Pakistan's burgeoning population of 211 million. The quality of forage available for livestock emanating largely from crop residues needs to be improved to meet the country's greenhouse-gas production targets in line with international expectation. The challenge remains for governments to sustain marketing chains that allow them to be profitable when operating in an increasingly hostile environment. The conservation of soil fertility through increased carbon sequestration will be an important imperative. It is likely that females will play a more important role in directing adaptation in these communities. Successful adjustment will be dependent on effective extension programs working with all sectors of the community including males, females and children from all walks of life in both rural and urban environments. Failure to do so will lead to rapid increases in climate refugee numbers, which the world can ill-afford. [ABSTRACT FROM AUTHOR]

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, each one introducing their own methodology. A frequent problem, though, is the use of suboptimally trained models as a first building block, leading to doubts about whether proposed approaches bring gains if applied to more sophisticated pretrained models. In this work, we propose a simple way to train such models, with the aim of reaching top performance on multiple standardized benchmarks in the field. This methodology offers a new baseline on which to propose (and fairly compare) new techniques or adapt existing ones. [ABSTRACT FROM AUTHOR]

Non-paddy cropping systems play a significant role in food production. However, excessive nitrogen loss from non-paddy soils through nitrate leaching and ammonia volatilization poses a significant challenge to environmental sustainability. In this study, microcosm and field-scale experiments were conducted to explore the potential for using hydrogen peroxide (H 2 O 2 ) to mitigate nitrogen loss and greenhouse gas emissions, aiming at filling gaps in knowledge regarding the underlying biochemical mechanisms. The results show that input of micromolar H 2 O 2 from either artificial addition or natural rainwater into soils in the presence of magnetite (Fe 3 O 4 ) could trigger Fenton-like reaction, which inhibited microbially mediated nitrification of soil-borne ammonium but did not affect the growth of the test crop plant (water spinach). In the absence of Fe 3 O 4 , input of rainwater-borne H 2 O 2 into non-paddy soils caused reduction in the emissions of nitrous oxide (N 2 O) and carbon dioxide (CO 2 ). There was a trend showing that the degree of reduction in N 2 O and CO 2 fluxes increased with increasing concentration of rainwater-borne H 2 O 2 . It was likely that microbially mediated reduction of iron oxides took place during rainfall events, providing Fe(II) that is needed for reaction with rainwater-borne H 2 O 2 , triggering Fenton-like reaction to inhibit the soil microbes that mediate production of N 2 O and CO 2 in the soils. The findings obtained from this study have implications for developing strategies to manage soil‑nitrogen to minimize its environmental impacts., (Copyright © 2024. Published by Elsevier B.V.)

This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I–F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117–0.215 and 0.701–0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme.

The feedback process in the contemporary world is done on a timely basis filled particularly by the individual concerned. This hectic procedure often turns out to be peer-driven jeopardization of the primary objective of the process. To prevent this vulnerability, this work proposes a dynamic method of generating feedback automatically based on emotion classification by nonlinear logistic regression model and neural network-based convolutional neural networks (CNN). For a given test sample, our working project detects multiple faces followed by the cropping of these detected faces and finally, these cropped faces are stored in a destination folder. Iterating through the contents in this destination folder one by one, first, the binary classifier logistic regression gives a probabilistic output in the form of a percentage, the level of interest found on the concerned cropped facial image. Second, these iterated contents are passed on to the sophisticated CNN model, having the capability to detect and extract specific emotion features from an image. The CNN gives a detailed analysis report of the concerned individual by classifying them into emotions like Anger, Disgust, Contempt, Happiness, Neutral, Surprise or Fear. The outputs of these two models that are machine-generated feedback, would effectively encourage organizational, structural or end-user policy changes necessary to develop and evolve in the current competitive world. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

In this study we propose a new approach to tackle the cropping problem in steganography which is called Center Embedded Pixel Positioning (CEPP) which is based on Least Significant Bit (LSB) Matching by setting the secret image in the center of the cover image. The evaluation of the experiment indicated that the secret image can be retrieved by a maximum of total 40% sequential cropping on the left, right, up, and bottom of the cover image. The secret image also can be retrieved if the total asymmetric cropping area is 25% that covered two sides (either left-right, left-up or right-up). In addition, the secret image can also be retrieved if the total asymmetric cropping area is 70% if the bottom part is included. If asymmetric cropping area included three sides, then the algorithm fails to retrieve the secret image. For cropping in the botom the secret image can be extracted up to 70%.

Wheat is a staple crop that is grown across the world due to its substantial contribution to human nutrition. Its significance is evident as it provides almost 20% of calories and protein required for daily human consumption. However, wheat yield is affected by rust disease that can reduce 30% of wheat production which is a serious threat to food security. In order to minimize the loss, it is crucial to identify precisely and localize the wheat rust disease and its infection types. For this purpose, several classification and segmentation techniques are used which are based on machine/deep learning models. This paper provides a realistic analysis and evaluation of various segmentation techniques including Watershed, Grab Cut, and U2-Net. These techniques are applied to the wheat stripe rust data to generate multiple datasets such as Watershed segmented data, GrabCut segmented data, and U2-Net segmented data. Subsequently, a pre-trained deep learning model, ResNet-18 is applied to these datasets to assess the impact of segmentation on classification accuracy. The highest classification accuracy (96.196%) is achieved on the dataset segmented by U2-Net. This research collates several state-of-the-art segmentation techniques in terms of correctness and their direct impact on classification accuracy which gives a pragmatic analysis for researchers to choose optimal segmentation technique. The research primarily focuses on the direct impact of segmentation on classification accuracy of wheat stripe rust, which has not been given sufficient focus in earlier researches.

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Synthesis and analysis of past cropping research can provide valuable information to direct future decisions around crop management. Systematic reviews and meta-analyses are considered gold standards in the synthesis and analysis of scientific research because they distil large amounts of information about complex issues, provide a summary of knowledge to date, and identify knowledge gaps. However, several issues concerning the methodologies employed to conduct systematic reviews have been identified; among them is the risk of publication bias when a review relies too heavily on 'white' literature from published academic sources and in so doing fails identify relevant 'grey' literature. Grey literature is inherently difficult to identify and collect, but forms a large portion of information available in many fields including agricultural-based research within Australia. The Online Farm Trials (OFT) database is a digital database of crop research field trial data from across Australia that has the potential for use as a discipline-specific source of grey literature to inform systematic reviews and meta-analyses. Using a case study approach to investigate the amount of information available on time of sowing (sowing date) on crop yield across Australia, we demonstrate that the OFT database provides easy access to transparent and reproducible search results similar to other commonly used academic databases. Systematic reviews and meta-analyses are considered gold standards in the synthesis and analyses of scientific research, but search methods often rely heavily on 'white' literature from published academic sources, and do not identify relevant 'grey' literature. Here, we show that the Online Farm Trials database provides an easily searchable source of relevant grey literature that should be used for future reviews and analyses of Australian grain crops. [ABSTRACT FROM AUTHOR]

Maximising student employability on graduation by ensuring they have the discipline knowledge as well as the 'soft skills' required by employers is an important focus of university courses. Following completion of an Agribusiness or Agricultural Science degree, many students enter the workforce in industries where they are required to run their own field trials to test new products or varieties, or to research agronomic best practise. One of the approaches highlighted in this paper is to incorporate Work Integrated Learning (WIL), including authentic assessment, into unit learning outcomes where the practical component of a unit is focused on developing industry required skills, such as field trial management, data collection, analysis and report writing, as well as embedding core discipline knowledge. Students, working in small groups, are required to run their own research field trial over a semester, having been guided to develop their research question based on a current industry issue on a presown crop, identify the measurements required to answer the question, and then plan their semester. At the end of the semester, the students present their work to the class and submit a conference-style research paper. Success is measured in relation to a clear hypothesis, measurements and analysis that addresses their hypothesis, and results related to the industry issue. Anecdotal feedback from students is that they enjoy the experience and responsibility of running their own trials, are able to add a skill to their curriculum vitae, and have increased confidence in their ability when entering the industry. The aim of this paper is to present an example of unit-level WIL, including authentic assessment that contains the application of discipline skills in crop science. [ABSTRACT FROM AUTHOR]

Characteristics of growth and cropping (trunk cross-sectional area, yield per tree, yield efficiency, biennial bearing index, and fruit weight) were studied in 25 late-season plum cultivars in the Belgrade area during the eight-year period (2012-2019). The cultivar 'Stanley' was used as a control for comparison. The period of study was divided into initial bearing (age of trees: four and five years) and full bearing (age of trees: between six and eleven years). Significant differences in yield among cultivars were found in both initial and full bearing periods. The average yield per tree in the period of initial bearing ranged from 1.6 kg in 'Pozna Plava' to 10.9 kg in 'Topper', and from 12.3 kg in 'Pozna Plava' to 26.1 kg in 'Mildora' in the period of full bearing. Compared with the control cultivar, significantly lower yields in the full bearing period were obtained in eight cultivars, while higher vigour, expressed as trunk cross-sectional area (TCSA), was found in ten cultivars. Cumulative yield efficiency varied between 0.46 kg/cm2 in 'Požegača' and 1.90 kg/cm2 in 'Topper'. Biennial bearing index was the lowest (0.06) in 'Nada', and the highest (0.90) in 'Tophit'. Fruit weight ranged from 17.9 g in 'Požegača' to 57.0 g in 'Empress'. Compared with the control, fruit weight was significantly higher in seven cultivars, and significantly lower in nine cultivars. On the basis of high yields and large fruit size, the cultivars 'Bluefre', 'Empress', 'Jojo', 'Nada', 'President' and 'Tophit' can be recommended for growing in the Belgrade region.

Context: In Australia's high-rainfall zone (>550 mm/year), winter-type canola (Brassica napus L.) can be sown in spring and grazed as a forage crop during its vegetative phase, producing a canola grain crop in late spring of the second year. The timing, length and intensity of grazing or defoliation that can be imposed on canola before a grain yield penalty occurs remains largely unknown. Aim: Our research aimed to determine the impact of grazing spring sown canola during summer and autumn on the subsequent canola grain yield. Methods: A paddock-scale field experiment was conducted over 2 years, 2013–14 and 2015–2016, at Hamilton, Victoria, Australia, with two winter-type canola treatments and three grazing–defoliation sub-treatments replicated four times in a replicated block design. The canola cultivars used were Hyola971CL and Taurus in 2013–14 and Hyola971CL and Brazzil in 2015–16. The three grazing–defoliation sub-treatments were: NG, no grazing; VG, grazing with sheep for a 10-week period during the vegetative phase over summer–autumn; RG, grazing with sheep as per the VG sub-treatment and then in the reproductive phase, either cutting the canola for silage in August (2013–14) or grazing with sheep between 28 July and 17 August 2016 (2015–16). Key results: The VG sub-treatment resulted in no grain-yield penalty (P > 0.05) relative to NG in either year. In 2014, canola grain yields from VG for Hyola971CL and Taurus were 2.32 and 2.13 t/ha, compared with NG grain yields of 2.66 and 2.19 t/ha. In 2016, grain yields from VG for Hyola971CL and Brazzil were 2.14 and 1.74 t/ha, compared with NG grain yields of 1.87 and 1.90 t/ha. The RG sub-treatment reduced (P < 0.05) canola grain yield by ~65% in 2014 and 80% in 2016 compared with NG and VG. Conclusions: Spring-sown winter-type canola can be grazed during summer and autumn in southern Australia with no grain-yield penalty relative to ungrazed canola. However, further grazing or defoliation in winter once bud elongation commences can result in large grain-yield penalties. Implications: Spring-sown winter-type canola is a viable forage option for filling the summer–autumn feed gap in southern Australia while also providing a dual-purpose income in mixed-farming systems. In southern Australia, summer–autumn is a period of feed shortage for livestock producers, with lack of green feed limiting livestock production or increasing farm expenditure on supplementary sources of feed. Winter-type canola can be sown in spring and grazed during its vegetative phase in summer and autumn, thus providing a source of green feed at a time when feed is limiting. If correct grazing management is applied, the canola can then produce a grain yield without penalty in its second spring, thus benefiting livestock producers by filling the summer–autumn feed gap and providing a second income stream in a mixed-farming system. [ABSTRACT FROM AUTHOR]

Context: Economic modelling identified that mating of ewe lambs for lambing at 1 year of age has the potential to increase the profitability of sheep-production systems in south-western Victoria. In order to optimise reproductive rates, ewe lambs should weigh at least 35–40 kg, have a condition score (CS) of 3.0 at mating, and be gaining weight (100–150 g/day) during mating. However, typical perennial ryegrass (Lolium perenne L.) pastures in south-west Victoria do not support the attainment of these targets without additional supplementation. Aim: Our research aimed to determine how grazing summer-active pastures or brassica (Brassica napus L.) forages affects the liveweight, CS, conception and reproductive rate of ewe lambs when offered before and during mating compared with a system using perennial ryegrass plus supplement. Methods: A field experiment was conducted at Hamilton, Victoria, Australia, in 2014 and 2016 testing seven forage treatments replicated four times in a complete block design. The treatments were: canola (B. napus , two treatments of different cultivars), forage brassica, lucerne (Medicago sativa L.), chicory (Cichorium intybus L.), plantain (Plantago lanceolata L.) and perennial ryegrass. Key results: Ewe lambs grazing canola or forage brassica had higher (P < 0.05) liveweight gains during the pre-mating and mating periods than those grazing the perennial ryegrass treatment (148 vs 75 g/day in 2014, s.e.m. 17; 139 vs 54 g/day in 2016, s.e.m. 17). In 2014, spring-sown dual-purpose canola, lucerne and chicory resulted in higher (P < 0.05) reproductive rates (144–151%) than the perennial ryegrass treatment (103%); forage brassica and plantain gave intermediate results (128% and 129%). Conclusion: Brassica forages (including spring-sown canola), lucerne and chicory can support the attainment of key liveweight and CS targets for successful mating of ewe lambs in autumn. Implications: Spring-sown canola is therefore a viable alternative forage for use by livestock producers in southern Australia and presents an opportunity to incorporate an alternative income stream in mixed-farming systems. Mating of ewe lambs for lambing at 1 year of age has the potential to increase the profitability of sheep-production systems; however, successful mating of ewe lambs requires that they have reached a bodyweight of 35–40 kg, have a condition score of at least 3.0, and be gaining weight before and during mating. Grazing dual-purpose canola crops during summer and autumn enables ewe lambs to meet these targets and improves liveweight gains and reproductive performance relative to traditional perennial ryegrass pastures. The implications are a more productive ewe flock, and a flock that produces more lambs over the duration of a ewe's lifetime. [ABSTRACT FROM AUTHOR]

Droughts affect a range of economically important sectors but their impacts are usually most evident within agriculture. Agricultural impacts are not confined to arid and semi-arid regions, but are increasingly experienced in more temperate and humid regions. A transferable drought management framework is needed to transition from coping to adapting to drought through supporting improved planning and policy decision-making through the supply chain from primary producers to consumers. A combination methodology using a Driver-Pressure-State-Impact-Response (DPSIR) approach, an analysis of weekly agricultural trade publications and semi-structured interviews were used to explore drought impacts and responses, using the 2018 United Kingdom drought as a case study. While most reported responses were on-farm, a diverse range of measures were implemented across institutional scales and through the supply chain, reflecting complex interactions within the food system. However, drought responses were dominated by reactive and crisis-driven actions to cope with, or enhance the recovery from, drought; but which contributed little to increased resilience to future droughts. Our transferable drought management framework shows how improved collaboration and multi-sector engagement across spatial, governance and supply-chain scales to develop human and social capital can enable the transition from coping (short-term and reactive) to adapting (long-term and anticipatory) strategies to increase agricultural resilience to future droughts.

Organic grain producers are interested in reducing tillage to conserve soil and decrease labor and fuel costs. We examined agronomic and economic tradeoffs associated with alternative strategies for reducing tillage frequency and intensity in a cover crop–soybean (Glycine max L. Merr.) sequence within a corn (Zea mays L.)–soybean–spelt (Triticum spelta L.) organic cropping system experiment in Pennsylvania. Tillage-based soybean production preceded by a cover crop mixture of annual ryegrass (Lolium perenne L. ssp. multiflorum), orchardgrass (Dactylis glomerata L.) and forage radish (Raphanus sativus L.) interseeded into corn grain (Z. mays L.) was compared with reduced-tillage soybean production preceded by roller-crimped cereal rye (Secale cereale L.) that was sown after corn silage. Total aboveground weed biomass did not differ between soybean production strategies. Each strategy, however, was characterized by high inter-annual variability in weed abundance. Tillage-based soybean production marginally increased grain yield by 0.28 Mg ha−1 compared with reduced-tillage soybean. A path model of soybean yield indicated that soybean stand establishment and weed biomass were primary drivers of yield, but soybean production strategy had a measurable effect on yields due to factors other than within-season weed–crop competition. Cumulative tillage frequency and intensity were quantified for each cover crop—sequence using the Soil Tillage Intensity Rating (STIR) index. The reduced-tillage soybean sequence resulted in 50% less soil disturbance compared to tillage-based soybean sequence across study years. Finally, enterprise budget comparisons showed that the reduced-tillage soybean sequence resulted in lower input costs than the tillage-based soybean sequence but was approximately $114 ha−1 less profitable because of lower average yields. [ABSTRACT FROM AUTHOR]

The evolution of image retargeting technique demands the exploitation of multi‐operators since they are capable of preserving the structure and salient objects of the image. However, these multi‐operators are mostly based on seam carving with scaling or cropping operators which lead to significant distortions in the retargeted image. This study proposes a new multi‐operator scheme which has improved seam carving, through the proposed seam diversion based image retargeting algorithm, integrated with the cropping and warping operator. A total of six different multi‐operator schemes have been proposed out of which the MO6 technique gave remarkable results in terms of image quality, least distortion, and lowest run‐time. To simplify image retargeting operations, an optimised image distance function was used. The optimised image distance function was formulated which combines bidirectional image Euclidean distance, dominant colour descriptor, and an energy‐based coefficient to bypass seams from the point where seams start clashing and hence the defined threshold violates. By integrating cropping and warping into the proposed algorithm, it preserves the salient features of the retargeted images. Typical results have been presented which demonstrates the effectiveness of the proposed methods. User‐based subjective analysis has also been carried out which shows that image retargeted using the MO6 technique has high user preference. [ABSTRACT FROM AUTHOR]

Background Despite of the importance of soils in agronomy, to date no comprehensive assessment of cropping in Europe has been performed from the viewpoint of the soil variability and its relationship to cropping patterns. In order to fill this knowledge gap, we studied the cropping patterns in different soils of European climate zones with regards to the shares of their crop types in a comparative manner. The study highlights the main features of farming by soil in Europe. Farming by soil in this context means the consideration of soil characteristics when selecting crop types and cropping patterns. Methods We first assessed the dissimilarity between the cropping compositions of different pedoclimatic zones in Europe. Next, we assessed the differences of crop distribution in the climate zones by soil types and main crop types by analyzing the degree of association of crops to soil types. A detailed country scale assessment was performed using crops-specific soil productivity maps and land use survey data from Hungary. Results Results suggest that, in general, farmers consciously take pedoclimatic condition of farming into account when selecting their cropping patterns. In other words, farming by soil is a common practice in the different climatic regions of Europe. However, we have strong reasons to believe that soil suitability-based cropping is not practiced to its full potential over the continent. For example, the findings of our European assessment suggest that production areas of legumes are not always optimized for the local pedoclimatic conditions in some zones. These findings also underline that economic drivers are decisive, when farmers adopt their cropping (eg. oil crops on Albeluvsiols in Europe). Win-win situations of economic considerations and soil suitability based management are observed in all pedoclimatic zones of Europe. The country analysis shows that cropping is progressively practiced on more suitable areas, depending also the crop tolerance to variable pedoclimatic conditions In conclusion, we can assume that pedoclimatic conditions of cropping are respected in most of Europe and farmers crops according to edaphic conditions whenever economic considerations do not override the ecological concerns of farming.

A meticulous image processing workflow is oftentimes required to derive quality image data from high-resolution, unmanned aerial systems. There are many subjective decisions to be made during image processing, but the effects of those decisions on prediction model accuracy have never been reported. This study introduced a framework for quantifying the effects of image processing methods on model accuracy. A demonstration of this framework was performed using high-resolution hyperspectral imagery (RMSE) of the estimators. A sensitivity analysis revealed that the segmentation step was the most influential image processing step on the final estimator error. Across all workflow scenarios, the RMSE of predicted nitrogen uptake ranged from 14.3 to 19.8 kg ha−1 (relative RMSE ranged from 26.5% to 36.5%), a 38.5% increase in error from the lowest to the highest error workflow scenario. The framework introduced demonstrates the sensitivity and extent to which image processing affects prediction accuracy. It allows remote sensing analysts to improve model performance while providing data-driven justification to improve the reproducibility and objectivity of their work, similar to the benefits of hyperparameter tuning in machine learning applications.

With the advancement in the field of image processing, images are being processed using various image processing algorithms. Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing. Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm. This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods. After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators. This survey is useful for the researchers interested in content-aware image retargeting. [ABSTRACT FROM AUTHOR]

The study was carried out in Batiaghata upazila of Khulna district to identify different climate resilient cropping patterns that had been practiced over the past three decades as well as the farmers' perception about the causes of adopting climate resilient cropping patterns. Information was collected through personal interview, focus group discussion, systematic study of available records and browsing internet. A slight change was observed regarding climate resilient cropping pattern over the last 30 years in the study area. About 30 years ago, five cropping patterns were practiced, only one pattern (Fallow-Fallow-T. Aman) was found climate resilient. About 20 years ago, 11 cropping patterns were practiced, 5 patterns (Fallow-Fallow-T. Aman, Boro-Fallow-T. Aman, Boro-Gher (vegetables)-T. Aman, Boro-Gher (vegetables)-Fallow and Fallow-Gher (vegetables)-T. Aman) were found climate resilient. About 10 years ago, 11 cropping patterns were practiced, 7 patterns (Sesame-Fallow-T. Aman, Boro-Fallow-T. Aman, Boro-Gher (vegetables)-Fallow, Fallow-Fallow-T.Aman, Boro-Jute-T. Aman, Boro-Gher (vegetables)-T. Aman and Fallow-Gher (vegetables)-T. Aman) were found climate resilient. At present, 14 cropping patterns are being practiced. Out of which, six patterns are climate resilient. Respondents justified nine causes for adopting climate resilient cropping patterns, while increase in cropping intensity was the most significant, while lower input cost was least significant. Most of the respondents (93.80%) had highly clear perception and the rest (6.20%) had moderately clear perception. A positive relationship of education and mass media exposure was observed with their perception. [ABSTRACT FROM AUTHOR]

In this work, a metal bar fine-cropping system is designed to study the influence load control strategies based on theoretical and cropping experiments. The principle of the new cropping system and mechanical compositions system are introduced. Three different load control strategies are designed based on duralumin alloy AlCu4Mg1 bar, and the optimal loading order is determined under them. According to a new proposed cross-section quality evaluation method, the test results show constant stress intensity factor range of external load (Δ K = 0. 75 K c ), linear decreasing cyclic load of appropriate time coefficient (T = - 2. 6) can obtain better cropping effect and the best quality is obtained by the loading cycle (iii). [ABSTRACT FROM AUTHOR]

The aim of this research was to identify the species composition and quantitative and qualitative structure of ground beetles inhabiting conventional potato cultivations. The study was conducted over the 2008 and 2011-2013 period in eleven localities in the Subcarpathian region (Podkarpacie). Beetles were captured using Barber pitfall traps. A total of 7406 beetles were caught, which were classified in 29 genera and 54 species. Small zoophages proved to be the most numerously represented trophic group, whereas the species that were caught as greatest numbers at the study sites were as follows: Harpalus rufipes, Pterostichus melanarius, and Poecilus cupreus. The obtained results demonstrate that anthropogenic factors affect both the trophic structure of ground beetle populations and their habitat preferences. At the same time, species with wide ecological tolerance and lower susceptibility to anthropogenic pressure were promoted. It was also shown that the maintenance and appropriate management of diverse biocoenoses in agricultural space contribute to increased diversity of Carabidae found in agrarian areas. [ABSTRACT FROM AUTHOR]

Abstract Background Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.