scholarly journals Development of Artificial Intelligence Climate Chamber and Experimental Study on Crop Phenotype Acquisition

2022 ◽  
Author(s):  
Anhua Ren ◽  
Dong Jiang ◽  
Min Kang ◽  
Jie Wu ◽  
Fangcheng Xiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Goodness Of Fit ◽  
Environmental Control ◽  
Canopy Temperature ◽  
Growth Period ◽  
Manual Measurement ◽  
System A ◽  
Application Rates ◽  
Fertiliser Application ◽  
Image Collection

Abstract Background: The deficiencies of traditional artificial climate chambers in phenotypic collection and analysis were improved to achieve the high-throughput acquisition of crop phenotypes during the growth period. This paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period. This research also established an environmental control system, a crop phenotype monitoring system and a crop phenotype acquisition system with environmental parameter adjustment and crop image collection. Phenotypic feature extraction and other functions were carried out in the cultivation experiment, and phenotype acquisition of wheat was performed under different nitrogen fertiliser application rates. Comparison and analyses were performed by the systematic and manual measurement values of crop phenotype characteristics, and the acquisition of wheat table was evaluated based on artificial intelligence climate cabin. The goodness of fit of the model was used to classify data.Results: During the different growth periods of wheat, the correlation analysis between the systematic and manual measurement values of its leaf area, plant height and canopy temperature showed that the obtained correlation coefficient r was greater than 1, and the fitting determination coefficient R2 was greater than 0.7156, with errors. The coefficient root mean square error was less than 2.42, indicating that the two were positively correlated, and their correlation was excellent. Conclusion: The results verified the feasibility and applicability of the artificial intelligence climate cabin to study the phenotypic characteristics of crops.

scholarly journals Towards optimal use of phosphorus fertiliser

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mart B. H. Ros ◽  
Gerwin F. Koopmans ◽  
Kees Jan van Groenigen ◽  
Diego Abalos ◽  
Oene Oenema ◽  
...  
Keyword(s):  
Agricultural Land ◽  
Meta Analysis ◽  
P Availability ◽  
Agricultural Systems ◽  
Application Rates ◽  
P Application ◽  
Fertiliser Application ◽  
Fertiliser Use ◽  
Yield Responses ◽  
Effective Use

Abstract Because phosphorus (P) is one of the most limiting nutrients in agricultural systems, P fertilisation is essential to feed the world. However, declining P reserves demand far more effective use of this crucial resource. Here, we use meta-analysis to synthesize yield responses to P fertilisation in grasslands, the most common type of agricultural land, to identify under which conditions P fertilisation is most effective. Yield responses to P fertilisation were 40–100% higher in (a) tropical vs temperate regions; (b) grass/legume mixtures vs grass monocultures; and (c) soil pH of 5–6 vs other pHs. The agronomic efficiency of P fertilisation decreased for greater P application rates. Moreover, soils with low P availability reacted disproportionately strong to fertilisation. Hence, low fertiliser application rates to P-deficient soils result in stronger absolute yield benefits than high rates applied to soils with a higher P status. Overall, our results suggest that optimising P fertiliser use is key to sustainable intensification of agricultural systems.

Patterns of macroparasite aggregation in wildlife host populations

Parasitology ◽  
1998 ◽  
Vol 117 (6) ◽  
pp. 597-610 ◽  
Author(s):  
D. J. SHAW ◽  
B. T. GRENFELL ◽  
A. P. DOBSON
Keyword(s):  
Poisson Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Data Sets ◽  
Frequency Distributions ◽  
System A ◽  
Host Sex ◽  
Host Parasite

Frequency distributions from 49 published wildlife host–macroparasite systems were analysed by maximum likelihood for goodness of fit to the negative binomial distribution. In 45 of the 49 (90%) data-sets, the negative binomial distribution provided a statistically satisfactory fit. In the other 4 data-sets the negative binomial distribution still provided a better fit than the Poisson distribution, and only 1 of the data-sets fitted the Poisson distribution. The degree of aggregation was large, with 43 of the 49 data-sets having an estimated k of less than 1. From these 49 data-sets, 22 subsets of host data were available (i.e. host data could be divided by either host sex, age, where or when hosts were sampled). In 11 of these 22 subsets there was significant variation in the degree of aggregation between host subsets of the same host–parasite system. A common k estimate was always larger than that obtained with all the host data considered together. These results indicate that lumping host data can hide important variations in aggregation between hosts and can exaggerate the true degree of aggregation. Wherever possible common k estimates should be used to estimate the degree of aggregation. In addition, significant differences in the degree of aggregation between subgroups of host data, were generally associated with significant differences in both mean parasite burdens and the prevalence of infection.

scholarly journals Body Reprogramming: Reframing the Fibromyalgia narrative and providing an integrative therapeutic model

2020 ◽  
Vol 7 (2) ◽  
pp. 205510292097149
Author(s):  
Anthony Fitzdonald Davies ◽  
Patrick Hill ◽  
Daniel Fay ◽  
Annily Dee ◽  
Cosima Locher
Keyword(s):  
Lived Experience ◽  
Goodness Of Fit ◽  
Current Evidence ◽  
Emergent Properties ◽  
Therapeutic Effects ◽  
Evidence Based ◽  
Illness Narrative ◽  
System A ◽  
Complex Adaptive ◽  
Therapeutic Model

We propose a theory known as the Hyland model to help conceptualise Fibromyalgia within a complex adaptive control system. A fundamental assumption is that symptom generating mechanisms are causally connected, forming a network that has emergent properties. An illness narrative has been developed which has a ‘goodness of fit’ with the lived experience of those with Fibromyalgia. The theory guides management within the clinical setting and incorporates current evidence-based therapeutic strategies, within a multi-modal intervention described as ‘Body Reprogramming’. This intervention focuses on non-pharmacological and lifestyle-based considerations. The theoretical framework also helps explain why modest therapeutic effects are gained from current pharmacological options.

Response of field-grown cotton (Gossypium hirsutum L.) to phosphorus fertilisation on alkaline soils in eastern Australia

Soil Research ◽  
2004 ◽  
Vol 42 (8) ◽  
pp. 913 ◽  
Author(s):  
C. G. Dorahy ◽  
I. J. Rochester ◽  
G. J. Blair
Keyword(s):  
Field Experiments ◽  
P Uptake ◽  
Lint Yield ◽  
Alkaline Soils ◽  
Critical Limit ◽  
Soil P ◽  
Application Rates ◽  
Eastern Australia ◽  
P Application ◽  
Fertiliser Application

Abstract. Seventeen field experiments were conducted on alkaline soils in eastern Australia between 1997 and 2000 to evaluate irrigated cotton response to phosphorus (P) fertilisation. Only 3 experiments demonstrated significant (P < 0.05) increases in crop P uptake or lint yield with P application. Comparison of several soil P tests revealed that Colwell (bicarbonate) P provided the best correlation with P uptake at early flowering and lint yield. Soil P may limit cotton growth where Colwell-P concentrations are <6 mg/kg. Soil P concentrations at most of the sites were well above this critical limit, so P fertiliser application was not required. Average P uptake at physiological cut-out and P removal in seed cotton was 21 and 15 kg P/ha, respectively. Apparent P fertiliser recovery was variable (0–67%) and may have contributed to the lack of response that was observed in 14 out of the 17 experiments. It is recommended that at least 40 kg P/ha be applied to soils with Colwell-P concentrations <6 mg/kg to increase soil P reserves. Application rates of at least 20 kg P/ha are recommended where Colwell-P falls between 6 and 12 mg/kg to maintain soil P fertility.

Artificial intelligence (AI) to improve patient outcomes in community oncology practices.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18098-e18098
Author(s):  
John Frownfelter ◽  
Sibel Blau ◽  
Ray D. Page ◽  
John Showalter ◽  
Kelly Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Goodness Of Fit ◽  
Predictive Analytics ◽  
Training Data ◽  
Model Specification ◽  
Care Plan ◽  
Cancer Type ◽  
Pilot Program ◽  
Data Set ◽  
Ecog Ps

e18098 Background: Artificial Intelligence(AI) for predictive analytics has been studied extensively in diagnostic imaging and genetic testing. Cognitive analytics adds by suggesting interventions that optimize health outcomes using real-time data and machine learning. Herein, we report the results of a pilot study of the Jvion, Inc. Cognitive Clinical Success Machine (CCSM), an eigen vector-based deep learning AI technology. Methods: The CCSM uses electronic medical record (EMR) and publicly available socioeconomic/behavioral databases to create a n-dimensional space within which patients are mapped along vectors resulting in thousands of relevant clusters of clinically/behaviorally similar patients. These clusters have a mathematical propensity to respond to a clinical intervention which are updated dynamically with new data from the site. The CCSM generates recommendations for the provider to consider as they develop a care plan based on the patients’ cluster. We tested and trained the CCSM technology at 3 US oncology practices for the risk (low, intermediate, high) of 4 specific outcomes: 30 day severe pain, 30 day mortality, 6 month clinical deterioration (ECOG-PS), and 6 month diagnosis of major depressive disorder (MDD). We report the accuracy of the CCSM based on the testing and training data sets. Area under the curve (AUC) was calculated to show goodness of fit of classification models for each outcome. Results: In the training/testing data set there were 371,787 patients from the 3 sites: female = 61.3%; age ≤ 50 = 21.3%, 51-65 = 26.9%, > 65 = 51.9%; white/Caucasian = 43.4%, black/African American = 5.9%, unknown race = 43.4%. Cancer types were unknown/missing for 66.3% of patients and stage for 90.4% of patients. AUC range per vector: 30 day severe/recurrent pain = 0.85-0.90; 30-day mortality = 0.86-0.97; 6-month ECOG-PS decline of 1 point = 0.88-0.92; and 6-month diagnosis of MDD = 0.77-0.90. Conclusions: The high AUC indicates good separation between true positives/negatives (proper model specification for classifying the risk of each outcome) regardless of the degree of missing data for variables including cancer type and stage. Following testing, a 6 month pilot program was implemented (06/2018-11/2018). Final results of the pilot program are pending.

scholarly journals Quantifying the interactions between defoliation interval, defoliation intensity and nitrogen fertiliser application on the nutritive value of rainfed and irrigated perennial ryegrass

2017 ◽  
Vol 68 (12) ◽  
pp. 1100 ◽  
Author(s):  
K. G. Pembleton ◽  
R. P. Rawnsley ◽  
L. R. Turner ◽  
R. Corkrey ◽  
D. J. Donaghy
Keyword(s):  
Perennial Ryegrass ◽  
Crude Protein ◽  
Nutritive Value ◽  
Grazing Management ◽  
Negative Consequences ◽  
Pasture Management ◽  
Leaf Stage ◽  
Application Rates ◽  
Acid Detergent Fibre ◽  
Fertiliser Application

A key goal of temperate pasture management is to optimise nutritive value and production. The influence of individual components such as irrigation, nitrogen (N) fertiliser, and grazing interval and intensity has been well researched, yet conjecture remains regarding practices that optimise pasture nutritive value, largely because interactions between inputs and grazing management have not been quantified. A 2-year, split-split-plot experiment was undertaken to investigate these interactions in a perennial ryegrass (Lolium perenne L.) dominant pasture at Elliott, Tasmania. Irrigation treatments (rainfed or irrigated) were main plots and defoliation intervals (leaf regrowth stage: 1-, 2- or 3-leaf) were subplots. Defoliation intensity (defoliation height: 30, 55 or 80 mm) and N fertiliser (0, 1.5 or 3.0 kg N/ha.day) were crossed within sub-subplots. Herbage samples were collected from each plot four times during the experiment and analysed for concentrations (% dry matter, DM) of neutral detergent fibre (NDF), acid detergent fibre (ADF) and crude protein (CP). Metabolisable energy (ME) concentration (MJ/kg DM) was estimated from these values. ME concentration decreased as defoliation height and interval increased for all time points except during winter. Crude protein concentration increased with increasing N fertiliser applications in the plots defoliated at the 1-leaf stage, but only as N applications increased from 1.5 to 3.0 kg N/ha.day for the plots defoliated at the 2- and 3-leaf stages. As N application rates increased from 0 to 1.5 kg N/ha.day, plots defoliated at the 3-leaf stage had greater increases in NDF concentration than plots defoliated at the 1-leaf stage, except during spring. As defoliation height and interval increased, ADF concentration increased in both spring and summer. Although defoliating at frequent intervals (1-leaf stage) and lower heights (30 mm) produced pasture of marginally higher nutritional value, these benefits are mitigated by the previously established, negative consequences of lower pasture yield and poor pasture persistence. Consequently, grazing management that maximises pasture productivity and persistence (i.e. defoliation between the 2- and 3-leaf regrowth stages to a height of 55 mm) should be applied to perennial ryegrass pastures irrespective of input management.

scholarly journals MANAGING ENVIRONMENT TO OPTIMIZE PRODUCTION AND MINIMIZE COSTS

HortScience ◽  
1990 ◽  
Vol 25 (9) ◽  
pp. 1182b-1182
Author(s):  
Maynard E. Bates
Keyword(s):  
Crop Production ◽  
New Technologies ◽  
Cultural Practices ◽  
Environmental Control ◽  
Canopy Temperature ◽  
Photon Flux ◽  
Plant Weight ◽  
Local Climate ◽  
Pests And Diseases ◽  
Artificial Intelligence Technology

Increased production and reduced costs are goals of all plant growers. As a rule, introduction of computer-based control of the plant environment in a well-designed greenhouse will result in yield increases of thirty percent (30%) over other control techniques. A simple model will show how these changes impact profitability.New technologies in sensors, interfaces, computers, software, and plant growth knowledge are being applied to management of the crop environment. Examples of direct canopy temperature measurement, online plant weight measurement, integration and control of photosynthetic photon flux, and nutrition control will be presented. Integrated process control is replacing setpoint maintenance. Models are being developed for incorporation into environmental control systems. Examples for solar irradiance and crop growth will be demonstrated.Ultimately expert systems based on artificial intelligence technology will manage crop production in controlled environments. These systems will incorporate information on crop genome, local climate, cultural practices, pests and diseases, economics, and markets, in addition to environmental control. A possible configuration of the hardware and software for such a system will be discussed.

scholarly journals Computer-Aided Diagnosis for Spitzoid Lesions Classification Using Artificial Intelligence Techniques

2021 ◽  
Vol 16 (1) ◽  
pp. 16-37
Author(s):  
Abir Belaala ◽  
Labib Sadek Terrissa ◽  
Noureddine Zerhouni ◽  
Christine Devalland
Keyword(s):  
Artificial Intelligence ◽  
Phase 1 ◽  
Computer Aided Diagnosis ◽  
Machine Learning Algorithms ◽  
Spitz Nevus ◽  
Artificial Intelligence Techniques ◽  
System A ◽  
Three Phase ◽  
Computer Aided ◽  
Aided Diagnosis

Spitzoid lesions may be largely categorized into Spitz Nevus, Atypical Spitz Tumors, and Spitz Melanomas. Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz Nevus and malignant Spitz Melanomas. Specifically, this paper aims to test several artificial intelligence techniques so as to build a computer-aided diagnosis system. A proposed three-phase approach is being implemented. In Phase 1, collected data are preprocessed with an effective SMOTE-based method being implemented to treat the imbalance data problem. Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase 2. Finally, in Phase 3, a 10-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification. Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy, specificity (0.98), and a sensitivity of 0.99.

scholarly journals Bacteria are more sensitive to nitrogen fertilizer application in tea plantation soil while fungi are more correlated to tea yield and quality

2021 ◽  
Author(s):  
Sheng Tang ◽  
Jingjie Zhou ◽  
Wankun Pan ◽  
Rui Tang ◽  
Qingxu Ma ◽  
...  
Keyword(s):  
Fungal Diversity ◽  
Environmental Changes ◽  
Partial Least Square ◽  
Fungal Communities ◽  
Soil Microbiome ◽  
Tea Plantation ◽  
Yield And Quality ◽  
Application Rates ◽  
Tea Plantations ◽  
Fertiliser Application

Abstract Aims Soil in tea plantations is characterised by severe acidification and high aluminium and fluorine content. Applying excessive nitrogen (N) is a common strategy in tea plantations. Fungal and bacterial responses to N fertiliser addition in tea plantations, especially their relationship with tea growth, quality, and soil microbiome composition, remain unclear. Methods We performed a field experiment using different N fertiliser application rates for 5 years (2016‒2020) in a tea-producing region of China. Results Application of excessive N (600 kg ha− 1 y− 1) reduced tea yield and quality. High N application rates (360 and 600 kg ha− 1 y− 1) significantly decreased bacterial and fungal diversity and altered the compositions of bacterial and fungal communities (P < 0.05). Fungi were more tolerant than bacteria to soil environmental changes induced by N fertiliser application. Succession of bacterial and fungal communities was mostly driven by pH. Partial least square path modelling suggested that N addition directly influenced the diversity and communities of bacteria and fungi, and indirectly influenced bacterial community and fungal diversity by mediating soil nutrients and pH. The assembly of fungal communities was more regulated by dispersal limitation and deterministic processes than that of bacterial communities. High microbial diversity was not a requirement for tea growth. Conclusions Fungi had a greater impact on tea yield and quality than bacteria; therefore, more attention should be given to fungi, which play a stable role in nutrient cycling and organic matter decomposition in tea plantation, eventually favouring tea growth.

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