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A response button that could register a mouse click was underneath each of the two boxes

The task consisted of 20 discrete choices between a smaller immediate reward presented in a box on the left side of the screen and an 80¢ delay reward presented in a box on the right side of the screen . At the top center of the screen was a box displaying total earnings on the task. On any trial, if the smaller sooner reward was selected with a single mouse click, the response options disappeared and a button appeared that stated “Click here to bank your amount.” Upon a single mouse click on this button, that amount was dispensed from the coin dispenser, and the total earnings box was updated. If the delayed 80¢ was selected, the response options disappeared and a number in the middle of screen counted down the number of seconds to wait . When the delay elapsed, a button appeared that required the participant to click to “bank” the 80 cents, at which point the coins were delivered and the total earnings were updated. When money was delivered, participants removed the coins from the dispensing tray and dropped them into a glass jar. There were 5 blocks of 4 trials each, with each block associated with a different delay for the 80 cent reward. The delays were 5, 10, 20, 40, and 80 s, and followed an increasing order across blocks. On the first trial of each block, the immediate reward size was 40¢ . The smaller reward was then adjusted within the block using a “decreasing adjustment” algorithm, which has been used in previous human studies involving hypothetical rewards . Specifically, the smaller sooner reward was adjusted by 20, 10, and 5¢ on Trials 2, 3, and 4 of the block, respectively, in the direction that would move choice toward indifference . The indifference point was defined as the value that would have been presented on a 5th trial had the algorithm continued . Indifference points therefore varied by increments of 2.5¢,vertical grow rack and were divided by 80¢ to be expressed as the proportion of the larger reinforcer. Indifference points were expressed as a proportion of the larger later reward .

A waiting period was imposed after the final trial to prevent participants from choosing the smaller immediate reward to end the task or session sooner . Participants were told before beginning the task that the total duration of the task would be independent of the choices made during the task, although participants were not explicitly told about the waiting period at the end of the task that was responsible for ensuring approximately equal task duration. The waiting period was defined as 660 s minus the sum of all larger reward delays that the participant experienced throughout the task. Although this manipulation ensured that total programmed waiting time did not substantially differ across participants, differences in participant response latency nonetheless allowed for some variability in total task time. At the end of the task, participants exchanged whole dollar amounts of coins for paper currency.The schizophrenia and control groups had qualitatively similar DD functions, but quantitatively, the schizophrenia group showed a significantly greater DD than controls on the experiential task, and normal DD on the hypothetical task. The schizophrenia group’s performance on the DD tasks was generally not associated with a range of potential confounds. In addition, test-retest reliability was examined for the schizophrenia group and was good on both tasks. These findings provide the first evidence of impaired DD in schizophrenia using an experiential paradigm that parallels tasks used in animal research much more closely than conventional human paradigms. While not all aspects of reward processing are impaired in schizophrenia , these findings suggest alterations do extend to a delay discounting context that involves real rewards and real delay periods. As described below, the schizophrenia group’s pattern of altered experiential and normal hypothetical DD likely reflects the fact the these tasks differed on several key dimensions, including reward type , reward magnitude , and delay time frame . Regarding qualitative analyses, the shape and orderliness of the DD data were generally similar across groups. In line with a prior report , the schizophrenia group showed typical hyperbolic discounting functions across tasks. Further, a large majority demonstrated orderly data for both DD tasks. The proportion with less-orderly data on the hypothetical, though not the experiential, task was significantly larger than controls.

However, the main study findings were unchanged after removing the subset of participants from both groups with less-orderly data. In this first study of experiential DD in schizophrenia, the schizophrenia group showed quantitatively greater discounting than controls for actual monetary rewards delivered in real time. Diminished discounting on this and similar experiential tasks has been reported in other clinical populations, including cocaine dependence, ADHD, and smokers . Experiential tasks appear to tap into a rather different aspect of DD than hypothetical tasks. For example, the correlation between hypothetical and experiential DD tasks was relatively small in both groups. Several studies have also reported relatively low convergence between these tasks and one found altered experiential but not hypothetical discounting in ADHD . There were no quantitative group differences for the hypothetical DD task and this study included the largest schizophrenia and control samples examined to date. Our finding on this task is consistent with two prior studies , but inconsistent with four others that found greater hypothetical DD in schizophrenia . The rather substantial methodological differences across the few DD studies make it difficult to pinpoint why three studies found normal DD but four did not. Since all prior studies included chronically ill samples, and all except one examined outpatients, the discrepancies across studies are not attributable to these participant characteristics. However, the tasks and data analytic approaches varied widely. For example, across the seven studies, the maximum delayed reward magnitude ranged from $86 to $1000, and the maximum delayed reward duration ranged from a few months up to 50 years. Further research will want to systematically assess the impact of these parameters on hypothetical DD in schizophrenia. For example, it could be informative to examine how individuals with schizophrenia perform on a hypothetical task with reward magnitudes and delay intervals that correspond to those in the experiential task. The current study considered a wide range of potentially confounding factors on DD and found that their impact was small. The only relevant factor was smoking status.

Smokers showed greater hypothetical DD than non-smokers, which converges with prior findings from the general population and schizophrenia . However, we still found the pattern of altered experiential and normal hypothetical DD in schizophrenia when we limited our analyses to non-smokers. There were no significant associations between DD and other substances, symptoms, or anti-psychotic medication dosages. Given the conceptual link between reward processing and negative symptoms , it is somewhat puzzling that alterations in DD, particularly on the experiential task, did not significantly correlate with higher clinically rated negative symptoms. Although some studies have found that neuroscience-based reward and decision making tasks are associated with negative symptoms a number of studies by our group and others failed to detect such relationships . The reason for these discrepancies is not year clear. We have suggested that there are complex intervening steps on the causal pathway between the relatively discrete processes measured by decision-making tasks and the broad aspects of experience and behavior that are captured by clinical rating scales,cannabis grow racks which may substantially diminish direct correlations . DD also showed no significant associations with global or particular domains of neurocognition. This does not support prior suggestions that DD disturbances in schizophrenia reflect problems in the representation and maintenance of reward value . The schizophrenia group’s pattern of altered experiential but normal hypothetical DD was also not attributable to differences in the test-retest reliabilities of the tasks. The test-retest correlations of approximately .70 for both tasks are similar to prior reports in healthy samples and the group means showed good stability across occasions. These findings, in conjunction with the lack of associations with symptoms, suggest the DD tasks are measuring relatively stable traits among individuals with schizophrenia. These properties support the use of the experiential DD task as a performance measure of decision-making impairment in clinical trials for schizophrenia . Its potential usefulness for clinical trials is bolstered by evidence that it is sensitive to state-related changes, such as sleep deprivation, dopamine agonist administration in Parkinson’s disease, alcohol administration, and methylphenidate administration in ADHD . One might have expected the schizophrenia group to show greater difficulties for hypothetical, distant rewards in light impaired abstract thinking and longer-term prospection associated with this disorder . However, the pattern found in the current study may relate to participant and task characteristics. Regarding participant characteristics, since schizophrenia is associated with decreased SES and many in the schizophrenia group were receiving limited fixed incomes, the schizophrenia group may have valued immediately available, real rewards more than controls. This possibility is bolstered by our finding that the schizophrenia group assigned higher value ratings than controls for the lowest value but similar ratings for the highest value on the subjective valuation of money index, and with previous research showing greater discounting in lower income adults . Although individual differences in subjective valuation ratings did not significantly correlate with performance on the DD tasks, this factor remains a possible contributor . Regarding task characteristics, Paglieri postulated key differences between hypothetical tasks and experiential tasks, beyond reward magnitude and delay length.

Whereas hypothetical tasks merely involve postponing receipt of a reward with no constraints on how subjects spend their time during the intervening delay, the waiting period in experiential tasks comes with associated costs. These include direct costs, such as boredom or discomfort, and opportunity costs, such as valuable activities that the participant could be engaged in if not forced to wait. The relevance of such costs was demonstrated in a recent study that found DD rates increased as an orderly function of the constraints on what people could do during the delay interval on a hypothetical task . Perhaps the individuals with schizophrenia in our study were hyper-responsive to the associated costs of doing nothing in the delay period and experienced alterations in their cost/benefit calculations. For example, schizophrenia is associated with an elevated tendency to experience negative affect/arousal and boredom , as well as altered decision-making on tasks that involve weighing the relative effort expenditure costs against monetary rewards . Studies that manipulate the constraints, or obtain subjective ratings/ psychophysiological measures, during delay intervals could shed light on the possible impact of these costs in DD in schizophrenia. Strengths of the current study include the large clinical sample, use of two different types of DD tasks, rigorous evaluation of data integrity, examination of many potential confounds, and evaluation of test-retest reliability. However, the study has some limitations and highlights areas in need of further study. First, participants with schizophrenia were taking medications at clinical dosages. Although dosage equivalents were not related to DD, the impact of medications remains unclear. Second, the schizophrenia sample was chronically ill and it is unknown whether similar DD patterns would be evident in younger or high-risk samples. Third, the order of delay discounting task administration was not counterbalanced, so we are unable to examine potential order effects. Fourth, although performance on the tasks was not related to subjective valuation of money, we did not obtain objective measures to evaluate whether income or socio-economic status was associated with DD task performance. Fifth, this study only assessed monetary rewards and it is unknown whether similar patterns would be found for other primary or secondary reinforcers. Sixth, although the schizophrenia group showed normal performance on the hypothetical DD task, we cannot tell if the normal choice patterns were achieved through abnormal neural processes. For example, a small fMRI study reported that individuals with schizophrenia showed an abnormal hypo-activation in some regions and hyper-activation in others while making DD decisions . Further attention to these issues can help clarify the nature of impaired reward processing and decision-making in schizophrenia. General Scientific Summary: Delay discounting refers to whether one is willing to forego a smaller, sooner reward for the sake of a larger, later reward. This study found that people with schizophrenia showed a greater preference for smaller, sooner rewards than healthy comparison participants on a DD task that involved making choices about actual monetary rewards provided in real time.

To definitively address this issue would require a blocking study in humans to estimate VND

We also found that attentional bias to threat mediated the relation between CB1 receptor availability in the amygdala and severity of threat symptomatology. These results extend a growing body of research demonstrating an association between trauma-related disorders such as PTSD, MDD, and GAD, and attentional bias to threat by implicating the CB1 receptor system as a key neurobiological underpinning of this endophenotype and its concomitant phenotypic expression of trauma-related threat symptomatology, particularly hyperarousal symptoms. They further suggest that attentional bias to threat may mediate the association between CB1 receptor availability in the amygdala and threat symptomatology, with greater CB1 receptor availability being linked to greater attentional bias to threat that is in turn linked to greater severity of threat symptomatology. Results of the current study build on extant neurobiological studies that have implicated the endocannabinoid system in the amygdala as an important modulator of anxiety , as well as functional activation of the amygdala in mediating attentional bias to threat among individuals with PTSD . Specifically, results of this study suggest that CB1 receptor availability in the amygdala may directly mediate this endophenotype and its associated phenotypic expression of trauma-related threat symptomatology. Preclinical work suggests that the activation of membrane glucocorticoid receptors appears to engage a G-protein-mediated cascade through the activation of Gs proteins that, in turn, increases the activity of cAMP and protein kinase A. This increase in protein kinase A appears to induce the rapid synthesis of an endocannabinoid signal through an as yet unknown mechanism that may be an increase in intracellular calcium signaling that is then released from principal neurons in the amygdala and activates CB1 receptors localized on the terminals of GABAergic neurons in the amygdala. It should be noted, however,vertical grow rack that other mechanisms than CB1 receptor stimulation by anandamide could contribute to the etiology of attentional bias to threat and threat symptomatology.

First, the two endocannabinoids anandamide and 2-arachidonoylglycerol have differential roles in endocannabinoid and have distinctly different metabolic pathways for anandamide and monoacylglycerollipase for 2-arachidonoylglycerol; . To date, the relative contribution of these two endocannabinoids and their pathways in the modulation of anxiety remains unclear. Furthermore, recent evidence suggests that CB1 receptor signaling varies across brain regions , and that diverse effects of anandamide–CB1 receptor signaling mechanisms are evident even within the extended amygdala . Finally, the actions of anandamide are not restricted to CB1 receptors, as endocannabinoids also act on CB2 receptors , GPR55 , transient receptor potential vanilloid type 1 channels , and other G-protein subtypes . Although additional research is needed to further evaluate how the endocannabinoid system mediates attentional bias to threat, the results of this study suggest that greater CB1 receptor availability in the amygdala, as well as lower levels of peripheral anandamide, are associated with a greater attentional bias to threat in trauma-exposed individuals. However, we acknowledge, that no human studies that we are aware of have found that anandamide concentrations directly influence CB1 receptor availability, and hence additional work is needed to ascertain how these variables are causally related. Nevertheless, the present data extend prior work linking attentional bias to threat to hyperarousal symptoms to suggest that the CB1 receptor system in the amygdala is implicated in modulating attentional bias to threat that is in turn linked to the transdiagnostic and dimensional phenotypic expression of trauma-related threat symptomatology. Further research will be useful in further elucidating molecular mechanisms that account for the observed association between CB1 receptor availability and the endophenotypic and phenotypic expression of threat processing in humans. An important question to be addressed in future work is whether pharmacotherapies that act on catabolic enzymes for endocannabinoids may be useful in the prevention and treatment of endophenotypic and phenotypic aspects of trauma-related threat symptomatology. Emerging evidence supports the potential utility of such targets, suggesting that variation in the FAAH gene is linked to reduced expression of FAAH that consequently results in elevations in circulating levels of anadamide , as well as decreased amygdala response to threat and more rapid habituation of the amygdala to repeated threat .

Notably, elevating anandamide levels via FAAH inhibition appear to provide a more circumscribed spectrum of behavioral effects than blocking MAGL that could potentially result in a more beneficial side effect profile, as anandamide is less prone to CB1 receptor desensitization and resultant behavioral tolerance . These classes of compounds are currently being investigated for their potential efficacy in treating mood and anxiety disorders. Given that core aspects of threat symptomatology such as hyperarousal are key drivers of more disabling aspects of the trauma-related phenotype such as emotional numbing , pharmacotherapeutic targeting of threat symptomatology in symptomatic trauma survivors may have utility in reducing the chronicity and morbidity of trauma-related psychiatric disorders such as PTSD, MDD, and GAD. Methodological limitations of this study must be noted. First, we studied a cohort of individuals with heterogeneous trauma histories. Although this is typical for most PTSD studies and we endeavored to recruit individuals who represented a broad and representative spectrum of traumarelated psychopathology, additional studies of samples with noncivilian trauma histories will be useful in extending these results. Second, 95% confidence intervals for coefficients in the mediation analysis were markedly wide, and hence additional studies in larger samples will be useful in ascertaining magnitudes of the observed associations. Third, we observed a high correlation between threat and loss symptomatology that may call into question the extent to which these symptom clusters reflect separable components of trauma-related psychopathology that are uniquely related to CB1 receptor availability in the amygdala and attentional bias to threat. Nevertheless, high correlations among symptom clusters of trauma-related psychopathology are not uncommon, with confirmatory factor analytic studies of substantially larger samples often observing inter correlations among symptom clusters 40.80 . Furthermore, the finding that CB1 receptor availability in the amygdala was associated only with threat, but not loss symptomatology, suggests greater specificity of association that accords with prior work . Fourth, it is important to recognize that our outcome measure in this study, VT, represents specific plus non-displaceable binding. Because of the lack of a suitable reference region devoid of CB1, we and others using different CB1 receptor ligands cannot directly calculate binding potential , a measure of specific binding. Thus, an implicit assumption in the interpretation of our results is that there are no group differences in VND, the distribution volume of non displaceable tracer uptake.

An alternative assumption would be that the magnitude of non displaceable binding is small compared with the total binding. To the best of our knowledge, such data are not currently available because of the lack of suitable selective CB1 antagonist drugs approved for human use. Blocking data with the CB1 receptor antagonist rimonabant in baboons , however,commercial vertical growing systems did show a large reduction in tracer uptake, suggesting that a substantial fraction of VT can be attributed to specific binding. Notwithstanding these limitations, the results of this study provide the first known in vivo molecular evidence of how a candidate neuroreceptor system—CB1—relates to attentional bias to threat and the dimensional expression of trauma-related psychopathology. Results revealed that greater CB1 receptor availability in the amygdala is associated with increased attentional bias to threat, as well as the phenotypic expression of threat-related symptomatology, particularly hyperarousal symptoms. Given that these results were based on a relatively small sample, further research in larger, transdiagnostic cohorts with elevated threat symptomatology will be useful in evaluating the generalizability of these results, as well as in examining the efficacy of candidate pharmacotherapies that target the anandamide–CB1 receptor system in mitigating both the endophenotypic and phenotypic expression of threat symptomatology in symptomatic trauma survivors.Recent large genome-wide association studies of substance use disorder phenotypes consistently show that there is only modest overlap [rg=0.38–0.77 ] between genetic factors that influence alcohol consumption and alcohol use disorder [AUD, ], whereas smoking and nicotine dependence are almost genetically identical [rg=0.95 ]. Importantly, the alcohol consumption and use disorder studies show divergent patterns of genetic association with other diseases . Whereas alcohol consumption is genetically correlated with higher educational attainment, lower body mass index and lower risk of coronary heart disease and type 2 diabetes; AUD and aspects of alcohol misuse share genetic associations with psychiatric disorders . In contrast, genetic correlation analyses show consistent associations of both CPD and ND with higher risks of psychiatric disorders, lower educational attainment, and higher risks of coronary heart disease or its predisposing factors . Sample sizes for GWAS of consumption phenotypes range from thousands to more than a million subjects from healthy volunteer collections, primarily from the GSCAN consortium, including UK Bio-bank and 23andMe, and the Million Veterans Program ; however, past GWAS of dependence phenotypes and genetic correlation analyses included smaller samples of some high-risk populations . Both ascertainment strategies can introduce bias. In this short communication, we are leveraging GWAS data from well-powered studies of consumption and misuse/dependence phenotypes.

Unlike previous studies, we focus largely on the UKB to control for potential selection biases, but also comparing results from non-UKB cohorts, and perform genetic correlations within a medical-center cohort from Vanderbilt University Medical Center . These analyses provide an evaluation of the degree to which the more easily and broadly obtained consumption phenotypes are good proxies for alcohol misuse and nicotine dependence. We used GWAS summary statistics largely from the UKB to control for potential selection biases that may differ across different cohorts. For alcohol phenotypes, we used GWAS summary statistics for alcohol consumption and misuse via the AUDIT from our previous work [UKB, N=121, 604 ]. For smoking phenotypes, we used GWAS summary statistics for CPD from GSCAN, for which 45.7% were UKB participants. For ND, we used GWAS summary statistics from 244,890 UKB participants with an International Classification of Disease code for ND . Because our measure of ND was binary, unlike all of our other quantitative variables, we also included data from a quantitative measure , available only from non-UKB cohorts in the Nicotine Dependence GenOmics consortium ]. We computed polygenic risk scores for the four alcohol and nicotine phenotypes using the PRS-CS “auto” version for each of the 66,915 genotyped individuals of European descent in BioVU. BioVU is one of the largest biobanks in the United States, consisting of electronic health record data from the Vanderbilt University Medical Center on ∼250,000 patients captured from 1990 to 2017. Genotyping and QC of this sample have been described elsewhere . In the genotyped BioVU sample, we fitted a logistic regression model to each of 1,335 case/control disease phenotypes to estimate the odds of each diagnosis given the PRS, after adjustment for sex, median age of the longitudinal electronic health record measurements, and first ten principal components of ancestry. Phenome-wide association study analyses were run using the PheWAS R package v0.12 . We required the presence of 100 cases with at least two or more ICD codes that mapped to a PheWAS disease category to assign “case” status. We used the standard Benjamini—Hochberg False Discovery Rate to correct for multiple testing. This threshold, however, is likely conservative because it incorrectly assumes independence between phecodes. To explore whether pleiotropic effects of the PRS were mediated by the diagnosis of tobacco use disorder , we also conducted PheWAS analyses using TUD as an additional covariate for each PRS. In addition, we repeated the PheWAS analyses using AUD diagnoses as additional covariates for each PRS. PRSs for both alcohol consumption and misuse were associated with AUD in BioVU . Of 1,335 phenotypes, PRSs for alcohol misuse were positively associated with other mental disorders, including mood disorders, major depressive disorder, bipolar disorder, and suicidal ideation or attempt, replicating previous findings using a PRS of a clinical alcohol dependence /AUD measure . In contrast, and replicating previous associations, alcohol consumption was negatively genetically correlated with metabolic conditions, such as type 2 diabetes and obesity. Adjusting the associations between alcohol consumption and metabolic disorders for AUD or TUD did not meaningfully change the magnitude of these associations, although the magnitude of the p-values increased slightly for some associations . Similarly, adjusting the associations with alcohol misuse use for AUD or TUD increased the magnitude of the p-value but the effect sizes remained largely unchanged .

Mechanical design and compliance have also been used to reduce the effects of variability and uncertainty

The large majority of robotic sensing applications involve proximal remote sensing, i.e., non-contact measurements – from distances that range from millimeters to a few tens of meters away from the target – of electromagnetic energy reflected, transmitted or emitted from plant or soil material; sonic energy reflected from plants; or chemical composition of volatile molecules in gases emitted from plants. Proximal remote sensing can be performed from unmanned ground vehicles or low-altitude flying unmanned aerial vehicles ; sensor networks can also be used . Current technology offers a plethora of sensors and methods that can be used to assess crop and environmental biophysical and biochemical properties, at increasing spatial and temporal resolutions. Imaging sensors that cover the visible, near-infrared , and shortwave infrared spectral regions are very common. A comprehensive review of non-proximal and proximal electromagnetic remote sensing for precision agriculture was given in . Proximal remote sensing technologies for crop production are reviewed in ; plant disease sensing is reviewed in detail in ; weed sensing is covered in , and pest/invertebrates sensing in . One type of sensing involves acquiring an image of the crop, removing background and non-crop pixels , and estimating the per-pixel biophysical variables of interest, or performing species classification for weeding applications. Estimation is commonly done through various types of regression . For example, during a training phase,grow vertical images of leaf samples from differently irrigated plants would be recorded, and appropriate spectral features or indices would be regressed against the known leaf water contents. The trained model would be evaluated and later used to estimate leaf water content from spectral images of the same crop.

Pixel-level plant species classification is done by extracting spectral features or appropriate spectral indices and training classifiers . In other cases, estimation of some properties – in particular those related to shape – is possible directly from images at appropriate spectra, using established image processing and computer vision techniques, or from 3D point clouds acquired by laser scanners or 3D cameras. Examples of such properties include the number of fruits in parts of a tree canopy , tree traits related to trunk and branch geometries and structure , phenotyping , shape-based weed detection and classification , and plant disease symptom identification from leaf and stem images in the visible range . Crop sensing is essential for plant phenotyping during breeding, and for precision farming applications in crop production. Next, the main challenges that are common to crop sensing tasks in different applications are presented, and potential contributions of robotic technologies are discussed.A major challenge is to estimate crop and environment properties – including plant detection and species classification – with accuracy and precision that are adequate for confident crop management actions. Wide variations in environmental conditions affect the quality of measurements taken in the field. For example, leaf spectral reflectance is affected by ambient light and relative angle of measurement. Additionally, the biological variability of plant responses to the environment can result in the same cause producing a wide range of measured responses on different plants. This makes it difficult to estimate consistently and reliably crop and biotic environment properties from sensor data. The responses are also often nonlinear and may change with time/plant growth stage. Finally, multiple causes/stresses can contribute toward a certain response , making it impossible for an ‘inverse’ model to map sensor data to a single stress source. Agricultural robots offer the possibility of automated data collection with a suite of complementary sensing modalities, concurrently, from large numbers of plants, at many different locations, under widely ranging environmental conditions.

Large amounts of such data can enhance our ability to calibrate regression models or train classification algorithms, in particular deep learning networks, which are increasingly being used in the agricultural domain and require large training data sets . Examples of this capability is the use of deep networks for flower and fruit detection in tree canopies, and the “See and Spray” system that uses deep learning to identify and kill weeds . Data from robots from different growers could be shared and aggregated too, although issues of data ownership and transmission over limited bandwidth need to be resolved. The creation of large, open-access benchmark data sets can accelerate progress in this area. Furthermore, sensors on robots can be calibrated regularly, something which is important for high-quality, reliable data. Other ways to reduce uncertainty is for robots to use complementary sensors to measure the same crop property of interest, and fuse measurements , or to measure from different viewpoints. For example, theoretical work shows that if a fruit can be detected in n independent images, the uncertainty in its position in the canopy decreases with n. Multiple sensing modalities can also help disambiguate between alternative interpretations of the data or discover multiple causes for them. New sensor technologies, such as Multi-spectral terrestrial laser scanning which measures target geometry and reflectance simultaneously at several wavelengths can also be utilized in the future by robots to assess crop health and structure simultaneously.Another major challenge is to sense all plant parts necessary for the application at hand, given limitations in crop visibility. Complicated plant structures with mutually visually occluding parts make it difficult to acquire enough data to reliably and accurately assess crop properties , recover 3D canopy structure for plant phenotyping or detect and count flowers and fruits for yield prediction and harvesting, respectively. This is compounded by our desire/need for high-throughput sensing which restricts the amount of time available to ‘scan’ plants with sensors moving to multiple viewpoints. Robot teams can be used to distribute the sensing load and provide multiple independent views of the crops. For example, fruit visibility for citrus trees has been reported to lie in the range between 40% and 70% depending on the tree and viewpoint , but rose to 91% when combining visible fruit from multiple perspective images .

A complementary approach is to utilize biology and horticultural practices such as tree training or leaf thinning, to simplify canopy structures and improve visibility. For example, when V-trellised apple trees were meticulously pruned and thinned to eliminate any occlusions for the remaining fruits, 100% visibility was achieved for a total of 193 apples in 54 images, and 78% at the tree bottom with an average of 92% was reported in . Another practical challenge relates to the large volume of data generated by sensors, and especially high-resolution imaging sensors. Fast and cheap storage of these data onboard their robotic carriers is challenging, as is wireless data transmission, when it is required. Application-specific data reduction can help ease this problem. The necessary compute power to process the data can also be very significant, especially if real-time sensor based operation is desired. It is often possible to collect field data in a first step, process the data off-line to create maps of the properties of interest , and apply appropriate inputs in a second step. However, inaccuracies in vehicle positioning during steps one and two, combined with increased fuel and other operation costs and limited operational time windows often necessitate an “on-the-go” approach,vertical grow systems where the robot measures crop properties and takes appropriate action on-line, in a single step. Examples include variable rate precision spraying, selective weeding, and fertilizer spreading. Again, teams of robots could be used to implement on-the go applications, where slower moving speeds are compensated by team size and operation over extended time windows.Interaction via mass delivery is performed primarily through deposition of chemical sprays and precision application of liquid or solid nutrients . Delivered energy can be radiative or mechanical, through actions such as impacting, shearing, cutting, pushing/pulling. In some cases the delivered energy results in removal of mass . Example applications include mechanical destruction of weeds, tree pruning, cane tying, flower/leaf/fruit removal for thinning or sampling, fruit and vegetable picking. Some applications involve delivery of both material and energy. Examples include blowing air to remove flowers for thinning, or bugs for pest management ; killing weeds with steam or sand blown in air streams or flame ; and robotic pollination, where a soft brush is used to apply pollen on flowers . Physical interaction with the crop environment includes tillage and soil sampling operations , and for some horticultural crops it may include using robotic actuation to carry plant or crop containers , manipulate canopy support structures or irrigation infrastructure . In general, applications that require physical contact/manipulation with sensitive plant components and tissue that must not be damaged have not advanced as much as applications that rely on mass or energy delivery without contact. The main reasons are that robotic manipulation which is already hard in other domains can be even harder in agricultural applications, because it must be performed fast and carefully, because living tissues can be easily damaged. Manipulation for fruit picking have received a lot of attention because of the economic importance of the operation .

Fruits can be picked by cutting their stems with a cutting device; pulling; rotation/twisting; or combined pulling and twisting. Clearly, the more complicated the detachment motion is, the more time-consuming it will be, but in many cases a higher picking efficiency can be achieved because of fruit damage reduction during detachment. Fruit damage from bruises, scratches, cuts, or punctures results in decreased quality and shelf life. Thus, fruit harvesting manipulators must avoid excessive forces or pressure, inappropriate stem separation or accidental contact with other objects .Contact-based crop manipulation systems typically involve one or more robot arms, each equipped with an end-effector. Fruit harvesting is the biggest application domain , although manipulation systems have been used for operations such as de-leafing , taking leaf samples , stomping weeds , and measuring stalk strength . Arms are often custom designed and fabricated to match the task; commercial, off-the-shelf robot arms are also used, especially when emphasis is given on prototyping. Various arm types have been used, including cartesian, SCARA, articulated, cylindrical, spherical and parallel/delta designs. Most reported applications use open-loop control to bring the end-effector to its target . That is, the position of the target is estimated in the robot frame using sensors and the actuator/arm moves to that position using position control. Closed-loop visual servoing has also been used to guide a weeding robot’s or fruit-picking robot’s end effector. End-effectors for fruit picking have received a lot of attention and all the main fruit detachment mechanisms have been tried .For example, properly-sized vacuum grippers can pick/suck fruits of various sizes without having to center exactly the end-effector in front of the targeted fruit . Also, a large variety of grippers for soft, irregular objects like fruits and vegetables have been developed using approaches that include from air , contact and rheological change . Once a fruit is picked, it must be transported to a bin. Two main approaches have been developed for fruit conveyance. One is applicable only to suction grippers and spherical fruits, and uses a vacuum tube connected to the end-effector to transport the picked fruit to the bin . In this case there is no delay because of conveyance, as the arm can move to the next fruit without waiting. However, the vacuum tube system must be carefully designed so that fruits don’t get bruised during transport. The other approach is to move the grasped fruit to some “home” location where it can be released to a conveyance system or directly to the bin. This increases transport time, which may hurt throughput. Clearly, there are several design and engineering challenges involved with this step.Combining high throughput with very high efficiency is a major challenge for physical interaction with crops in a selective, targeted manner; examples of such selective interactions are killing weeds or picking fruits or vegetables. For example, reported fruit picking efficiency in literature for single-arm robots harvesting apple or citrus trees ranges between 50% to 84%; pick cycle time ranges from 3 to 14.3s . However, one worker on an orchard platform can easily maintain a picking speed of approximately 1 apple per 1.5 seconds with efficiency greater than 95% . Hence, replacing ten pickers with one machine would require building a 10-40 faster robotic harvester that picks gently enough to harvest 95% of the fruit successfully, without damage, and do so at a reasonable cost!

What are the considerations for efficient water and nutrient use in large-scale cannabis cultivation to minimize environmental impact?

Efficient water and nutrient use in large-scale cannabis cultivation are essential for minimizing the environmental impact and promoting sustainability. Here are key considerations and strategies to achieve this:

  1. Water Management:a. Irrigation Efficiency:
    • Use drip or precision irrigation systems to deliver water directly to the root zone, vertical grow rack reducing wastage and minimizing runoff.Implement irrigation scheduling based on plant needs, climate conditions, and soil moisture monitoring.
    b. Water Recycling:
    • Invest in water capture and recycling systems to reuse irrigation runoff and rainwater.Implement closed-loop systems to minimize water loss.
    c. Water Quality:
    • Regularly test and monitor water quality to ensure it meets the needs of cannabis plants and does not introduce contaminants.
    d. Mulching:
    • Apply mulch around plants to reduce evaporation and maintain soil moisture levels.
    e. Drought-Resistant Cultivars:
    • Consider selecting cannabis strains that are more drought-tolerant to reduce water requirements.
  2. Nutrient Management:a. Soil Testing:
    • Conduct regular soil tests to assess nutrient levels and adjust fertilizer applications accordingly.
    b. Balanced Fertilization:
    • Apply fertilizers in the right ratios to match plant nutrient requirements, preventing excess runoff and leaching.
    c. Organic and Slow-Release Fertilizers:
    • Use organic and slow-release fertilizers that release nutrients gradually, reducing the risk of nutrient imbalances and environmental pollution.
    d. Microbial Inoculants:
    • Incorporate beneficial microbes into the soil to improve nutrient availability and uptake by plants.
    e. Precision Nutrient Delivery:
    • Implement precision nutrient delivery systems to target the root zone and minimize wastage.
    f. Fertigation:
    • Combine irrigation and fertilization through a fertigation system to improve nutrient uptake efficiency.
  3. Compost and Organic Matter:
    • Add compost and organic matter to the soil to enhance water retention and nutrient-holding capacity.
  4. Cover Crops:
    • Plant cover crops during non-cannabis growing seasons to prevent soil erosion, improve soil health, and reduce nutrient runoff.
  5. Regulatory Compliance:
    • Stay informed about local regulations and restrictions related to water use, nutrient management, and runoff control.
  6. Education and Training:
    • Train staff in efficient water and nutrient management practices to ensure compliance with best practices.
  7. Monitoring and Data Analysis:
    • Implement monitoring systems to track water and nutrient usage,cannabis grow racks allowing for data-driven decisions and optimization.
  8. Integrated Pest Management (IPM):
    • Implement a robust IPM program to prevent pest and disease outbreaks, reducing the need for excessive water and nutrient application due to plant stress.
  9. Energy Efficiency:
    • Use energy-efficient equipment for water management, such as pumps and irrigation systems.
  10. Environmental Impact Assessment:
    • Conduct regular environmental impact assessments to identify areas for improvement and track progress in reducing resource use and environmental impact.

By integrating these considerations and implementing sustainable practices, large-scale cannabis cultivation can reduce its environmental footprint, conserve water, and optimize nutrient use while still producing high-quality cannabis products.