Varying Lighting Conditions Research Articles

Catmint (Nepeta nuda L.) Phylogenetics and Metabolic Responses in Variable Growth Conditions.

Nepeta nuda (catmint; Lamiaceae) is a perennial medicinal plant with a wide geographic distribution in Europe and Asia. This study first characterized the taxonomic position of N. nuda using DNA barcoding technology. Since medicinal plants are rich in secondary metabolites contributing to their adaptive immune response, we explored the N. nuda metabolic adjustment operating under variable environments. Through comparative analysis of wild-grown and in vitro cultivated plants, we assessed the change in phenolic and iridoid compounds, and the associated immune activities. The wild-grown plants from different Bulgarian locations contained variable amounts of phenolic compounds manifested by a general increase in flowers, as compared to leaves, while a strong reduction was observed in the in vitro plants. A similar trend was noted for the antioxidant and anti-herpesvirus activity of the extracts. The antimicrobial potential, however, was very similar, regardless the growth conditions. Analysis of the N. nuda extracts led to identification of 63 compounds including phenolic acids and derivatives, flavonoids, and iridoids. Quantification of the content of 21 target compounds indicated their general reduction in the extracts from in vitro plants, and only the ferulic acid (FA) was specifically increased. Cultivation of in vitro plants under different light quality and intensity indicated that these variable light conditions altered the content of bioactive compounds, such as aesculin, FA, rosmarinic acid, cirsimaritin, naringenin, rutin, isoquercetin, epideoxyloganic acid, chlorogenic acid. Thus, this study generated novel information on the regulation of N. nuda productivity using light and other cultivation conditions, which could be exploited for biotechnological purposes.

Real-time indoor localization with visual SLAM for in-building emergency response

Localization is critical to but also challenging for an efficient first response during indoor emergency situations. This paper describes an economical indoor localization system with a low setup cost based on the visual Simultaneous Localization and Mapping (vSLAM). In addition, threshold for key features per image frame is studied for acceptable localization accuracy. The amount of image input users should collect is also analyzed for reliable localization. To address matching and accuracy issues under varying lighting conditions, a comprehensive approach for accurate localization utilizing vSLAM is also proposed. An actual building was tested for the proposed approach with an average localization success rate over 94%.

Blue and Red Light Color Combinations Can Enhance Certain Aspects of Digestive and Anabolic Performance in Juvenile Steelhead Trout Oncorhynchus mykiss

The light spectrum varies with the altitude of the sun and shows different light colors in clear water. In this study, we aimed to investigate the response of juvenile steelhead trout Oncorhynchus mykiss (34.67 ± 2.69 g initial weight) under different light color conditions. The effects of different blue and red light combinations on plasma biochemical parameters, digestive enzyme activity, and RNA/DNA ratio were assessed in trout over 16 weeks. Six treatments were randomly assigned to 24 tanks with four replicates per treatment: a constant light intensity of 150 lx: 12 h white light then 12 h dark (12W); 12 h blue light then 12 h dark (12B); 12 h red light then 12 h dark (12R); 1.5 h blue light, 9 h red light, 1.5 h blue light, then 12 h dark (3B9R); 3 h blue light, 6 h red light, 3 h blue light, then 12 h dark (6B6R); and 12 h of both blue and red light then 12 h dark (T12BR). Fish exposed to the 3B9R light environment showed significantly increased plasma levels of total protein (TP), enhanced activities of midgut lipase, trypsin, and gastric lipase; and increased RNA content in the liver and muscle tissue to promote protein synthesis efficiency, thereby improving digestive and anabolic performance compared to fish in the other treatments. This indicates that steelhead trout have adapted well to such variable light conditions during long-term evolution. In contrast, trout exposed to the 6B6R light environment showed significant reductions in plasma glucose, TP, and triglyceride levels, decreased activity of gastrointestinal digestive enzymes, and reduced protein synthesis capacity in the muscle and liver, resulting in weakened digestive and anabolic performance. Furthermore, despite the high RNA content and RNA/DNA ratio in fish exposed to a 12R light environment, relatively high plasma cholesterol and triglycerides levels were observed, which might indicate oxidative stress. Therefore, this light is not considered suitable for long-term cultivation. In conclusion, the 3B9R treatment was the optimal light condition tested and can be used to improve the digestive and anabolic performance of steelhead trout.

Fast and robust active camera relocalization in the wild for fine-grained change detection

Active camera relocalization (ACR) is an important and challenging task, whose feasibility and success highly depend on illumination consistency and convergence speed. If under varied lighting conditions in outdoor scenes, however, both the convergence and accuracy of ACR cannot be guaranteed. In this paper, we propose a fast and robust ACR scheme, namely rACR, that works well under highly varied illuminations. To achieve robustness to lighting variations, rather than using 2D feature matching, we rely on 3D point clouds, acquired by a visual SLAM engine (VSE), to register the current and reference camera coordinate frames. We present a scale-aware point cloud matching function that is minimized by a two-stage coarse-to-fine method, i.e., fast alignment considering only geometric error at first, followed by fine-grained alignment optimizing both geometric, photometric errors and the poses of VSE keyframes. The two aligned point clouds with equalized scales help to bridge current and reference observations, avoiding 2D feature matching that are sensitive to large lighting variances, and can directly generate effective camera pose adjustments. Moreover, to achieve fast convergence speed, we implement the above algorithm with a parallel scheme, which is specifically composed of an initialization procedure and three parallel threads, i.e., VSE thread, pose alignment thread, and pose adjustment thread. Extensive experiments show that, rACR has much higher robustness to lighting variations and 5× faster convergence rate over state-of-the-art methods, thus significantly improves its feasibility in real-world fine-grained change detection tasks in the wild.

Classification of vehicle types using fused deep convolutional neural networks

Classification of vehicle types using surveillance images is a challenging task in Intelligent Transportation Systems (ITS). In this paper, Convolutional Neural Networks for Vehicle types classification are comparatively studied. Firstly, GoogLeNet, ResNet50 and InceptionV4 are exploited as baselines for comparison. Secondly, we proposed a new network architecture based on GoogLeNet, ResNet50 and InceptionV4, named Fused Deep Convolutional Neural Networks (FDCNN), to take advantage of the ‘Inception’ module on parameter optimization and ‘Residual’ module on avoiding gradient vanishing, and applied the model to vehicle types classification. Thirdly, we created a vehicle dataset under the conditions of complicated and varied weather and lighting conditions, and conducted comparative experiments using the SEU vehicle dataset. Experimental results show much better performance of the proposed FDCNN with RMSprop optimizer on recognizing vehicle types. Specifically, the average classification accuracies of six vehicle types, such as truck, coach, sedan, minivan, pickup and SUV, are over 96.8%. Among the six classes of vehicle types, sedan is the most difficult to classify and the proposed FDCNN achieved over 93.81% accuracy in comparative experiments.

Does Fluctuating Light Affect Crop Yield? A Focus on the Dynamic Photosynthesis of Two Soybean Varieties.

In natural environments, plants are exposed to variable light conditions, but photosynthesis has been mainly studied at steady state and this might overestimate carbon (C) uptake at the canopy scale. To better elucidate the role of light fluctuations on canopy photosynthesis, we investigated how the chlorophyll content, and therefore the different absorbance of light, would affect the quantum yield in fluctuating light conditions. For this purpose, we grew a commercial variety (Eiko) and a chlorophyll deficient mutant (MinnGold) either in fluctuating (F) or non-fluctuating (NF) light conditions with sinusoidal changes in irradiance. Two different light treatments were also applied: a low light treatment (LL; max 650 μmol m−2 s−1) and a high light treatment (HL; max 1,000 μmol m−2 s−1). Canopy gas exchanges were continuously measured throughout the experiment. We found no differences in C uptake in LL treatment, either under F or NF. Light fluctuations were instead detrimental for the chlorophyll deficient mutant in HL conditions only, while the green variety seemed to be well-adapted to them. Varieties adapted to fluctuating light might be identified to target the molecular mechanisms responsible for such adaptations.

Influence of ambient light conditions on the accuracy and scanning time of seven intraoral scanners in complete-arch implant scans

PurposeThe purpose of this in vitro study was to evaluate the effect of ambient light illuminance on the accuracy and scanning time of different intraoral scanners (IOSs) in complete-arch implant scans. Material and methodsSeven IOSs (TRIOS 3, Primescan, Element 5D, i700, i500, CS3700, and CS3600) at 5 ambient lighting illuminances (100, 500, 1000, 5000, and 10 000 lux) were evaluated. An edentulous cast with 4 implants was selected as the master model. An implant scan body was tightened on each implant. The cast was digitized by using a laboratory scanner to obtain a reference standard tessellation language (STL) file, and 50 scans (10 per ambient light condition) were recorded with each IOS. Scanning time was recorded by using a digital chronograph. Intraoral scan deviations were calculated by using a 3D metrology software program (Geomagic Control X). Kruskal-Wallis and pairwise comparison tests were used to analyze the data (α=0.05). ResultsThe trueness and precision values obtained for each IOS tested were significantly different under the varying lighting conditions assessed. TRIOS 3 (34.0 ± 3.3 µm trueness; 24.5 ± 14.9 µm precision), Element 5D (34.5 ± 7.1 µm trueness; 25.9 ± 7.6 µm precision), and CS3700 (34.9. ±13.0 µm trueness; 34.6 ± 19.2 µm precision) performed better under 100 lux illumination, CS3600 (69.5 ± 24.0 µm trueness; 36.6 ± 20.1 µm precision) at 500 lux; i500 (36.2 ± 5.1 µm trueness; 21.4 ± 6.8 µm precision) at 1000 lux; i700 (34.8 ± 2.2 µm trueness; 15.4 ± 5.0 µm precision) at 5000 lux, and Primescan (37.4 ± 37.3 µm trueness; 26.2 ± 26.2 µm precision) at 10,000 lux. Additionally, the scanning time was different under different illuminance for each IOS. The fastest IOS in all light conditions was Primescan, with significant differences with all the groups (P<.01), followed by TRIOS 3 in all groups except under 100 lux illumination, where i700 was the second fastest. ConclusionsAmbient light influenced the accuracy and scanning time of IOSs assessed; however, the effect was not the same for all devices. It is necessary to optimize ambient light illuminance for each IOS to maximize scanning accuracy.

Art by firelight? Using experimental and digital techniques to explore Magdalenian engraved plaquette use at Montastruc (France).

Palaeolithic stone plaquettes are a type of mobiliary art featuring engravings and recovered primarily from Magdalenian sites, where they can number from single finds to several thousand examples. Where context is available, they demonstrate complex traces of use, including surface refreshing, heating, and fragmentation. However, for plaquettes with limited or no archaeological context, research tends to gravitate toward their engraved surfaces. This paper focuses on 50 limestone plaquettes excavated by Peccadeau de l’Isle from Montastruc, a Magdalenian rockshelter site in southern France with limited archaeological context; a feature common to many art bearing sites excavated across the 19th and early 20th Centuries. Plaquette use at Montastruc was explored via a programme of microscopy, 3D modelling, colour enhancement using DStretch©, virtual reality (VR) modelling, and experimental archaeology, the latter focusing on limestone heating related to different functional and non-functional uses. While the limited archaeological context available ensures the results remain only indicative, the data generated suggests plaquettes from Montastruc were likely positioned in proximity to hearths during low ambient light conditions. The interaction of engraved stone and roving fire light made engraved forms appear dynamic and alive, suggesting this may have been important in their use. Human neurology is particularly attuned to interpreting shifting light and shadow as movement and identifying visually familiar forms in such varying light conditions through mechanisms such as pareidolic experience. This interpretation encourages a consideration of the possible conceptual connections between art made and experienced in similar circumstances, such as parietal art in dark cave environments. The toolset used to investigate the Montastruc assemblage may have application to other collections of plaquettes, particularly those with limited associated context.

Triclosan uptake and transformation by the green algae Euglena gracilis strain Z

Triclosan is an antimicrobial chemical present in consumer products that is frequently detected in aquatic environments. In this research, we investigated the role of a common freshwater microalgae species, Euglena gracilis for triclosan uptake and transformation in open–water treatment wetlands. Lab–scale wetland bioreactors were created under various conditions of light (i.e., continuous (white) light, red light, and in the dark), media (i.e., wetland, autoclaved wetland, Milli–Q, and growth media water), and presence or absence of algae. Triclosan and its potential transformation products were identified in the water and algae phases. Triclosan transformation occurred most rapidly with reactors that received continuous (white) light, with pseudo first–order rate constants, k, ranging from 0.035 to 0.292 day−1. This indicates that phototransformation played a major role in triclosan transformation during the day, despite light screening by algae. Algae contributed to the uptake and transformation of triclosan in all reactors, and algae and bacteria both contributed to triclosan biotransformation under dark conditions, representative of nighttime conditions. Some transformation products were formed and further transformed, e.g., triclosan–O–sulfate, methoxy and diglucosyl conjugate of hydroxylated triclosan, and dimethoxy and glucosyl conjugate of 2,4–dichlorophenol, suggesting their minimal accumulation over the 25 days of the experiments. This study shows that the combined action of light, microbes, and algae allows the safe transfer and transformation of triclosan in open–water treatment wetlands.

Mercury Reduction, Uptake, and Species Transformation by Freshwater Alga Chlorella vulgaris under Sunlit and Dark Conditions.

As a major entry point of mercury (Hg) to aquatic food webs, algae play an important role in taking up and transforming Hg species in aquatic ecosystems. However, little is known how and to what extent Hg reduction, uptake, and species transformations are mediated by algal cells and their exudates, algal organic matter (AOM), under either sunlit or dark conditions. Here, using Chlorella vulgaris (CV) as one of the most prevalent freshwater model algal species, we show that solar irradiation could enhance the reduction of mercuric Hg(II) to elemental Hg(0) by both CV cells and AOM. AOM reduced more Hg(II) than algal cells themselves due to cell surface adsorption and uptake of Hg(II) inside the cells under solar irradiation. Synchrotron radiation X-ray absorption near-edge spectroscopy (SR-XANES) analyses indicate that sunlight facilitated the transformation of Hg to less bioavailable species, such as β-HgS and Hg-phytochelatins, compared to Hg(Cysteine)2-like species formed in algal cells in the dark. These findings highlight important functional roles and potential mechanisms of algae in Hg reduction and immobilization under varying lighting conditions and how these processes may modulate Hg cycling and bioavailability in the aquatic environment.

Wind dispersal of battery-free wireless devices.

Plants cover a large fraction of the Earth's land mass despite most species having limited to no mobility. To transport their propagules, many plants have evolved mechanisms to disperse their seeds using the wind1-4. A dandelion seed, for example, has a bristly filament structure that decreases its terminal velocity and helps orient the seed as it wafts to the ground5. Inspired by this, we demonstrate wind dispersal of battery-free wireless sensing devices. Our millimetre-scale devices weigh 30 milligrams and are designed on a flexible substrate using programmable, off-the-shelf parts to enable scalability and flexibility for various sensing and computing applications. The system is powered using lightweight solar cells and an energy harvesting circuit that is robust to low and variable light conditions, and has a backscatter communication link that enables data transmission. To achieve the wide-area dispersal and upright landing that is necessary for solar power harvesting, we developed dandelion-inspired, thin-film porous structures that achieve a terminal velocity of 0.87 ± 0.02 metres per second and aerodynamic stability with a probability of upright landing of over 95%. Our results in outdoor environments demonstrate that these devices can travel 50-100 metres in gentle to moderate breeze. Finally, in natural systems, variance in individual seed morphology causes some seeds to fall closer and others to travel farther. We adopt a similar approach and show how we can modulate the porosity and diameter of the structures to achieve dispersal variation across devices.

Oviposition site selection in the crepuscular moth, Grapholita molesta: does light matter?

AbstractSome insects that particularly prefer sunny or shaded sites use light intensity to locate suitable oviposition sites during the day. However, it is not known whether crepuscular species can be guided by light for oviposition under dim or variable light conditions. We used the crepuscular moth Grapholita molesta (Busck) (Lepidoptera: Tortricidae), a major pest of stone and pome fruits worldwide, to investigate oviposition response to light at various intensities (1000, 100, 10, and 1 mW m−2). Light intensity in sunny sites in a peach tree canopy is about 10× higher than in shaded sites. In both light vs. dark and brighter light vs. darker light choices, G. molesta females can discriminate differences in light intensity, exhibiting a strong preference for brighter light, independent of light intensity. Moreover, the light‐dependent oviposition responses did not change when exposed to odor stimuli. Our findings show that this crepuscular moth G. molesta can use light intensity to direct oviposition toward relatively sunny sites under dim light conditions. This study highlights the importance of vision for crepuscular species in oviposition site selection.

Portable locomotion activity monitor (pLAM): A cost‐effective setup for robust activity tracking in small animals

Abstract Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging, is often needed to get a complete picture of a species' ecology. Automated trackers are an invaluable tool for high‐throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial activity monitors can be expensive, closed source and generally limited to laboratory settings. Here, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity. Our setup uses inexpensive components, builds upon open‐source motion tracking software, and is easy to assemble and use in the field. It runs off‐grid, supports low‐light tracking with infrared lights and can implement arbitrary light cycle colours and brightnesses with programmable LEDs. We provide a user‐friendly guide to assembling pLAM hardware, accessing its pre‐configured software and guidelines for using it in other systems. We benchmarked pLAM for insects under various laboratory and field conditions, then compared results to a commercial activity detector. They offer broadly similar activity measures, but our setup captures flight and bouts of motion that are often missed by beam breaking activity detection. pLAM can automate laboratory and field monitoring of activity and timing in a wide range of biological processes, including circadian rhythm, eclosion and diapause timing, pollination and flower foraging, or pest feeding activity. This low cost and easy setup allows high‐throughput animal behaviour studies for basic and applied ecology and evolution research.

Ergonomic lighting considerations for the home office workplace.

The COVID-19 pandemic has resulted in a large increase in the use of the home office environment. Compared to traditional commercial offices, home offices typically have more variable lighting conditions that can affect the worker's performance and well-being. To review and discuss various lighting sources and their ergonomic impacts on the population of office employees now working from home. Specifically addressing the impacts of electronic light from screens, daylight, and task lighting's impact on health and well-being in the frame of the COVID-19 pandemic. Over 75 ergonomic and lighting literature publications covering various work environments were reviewed, with a focus on the health effects of screen light, daylight, and task light. Resulting from the literature review, design recommendations regarding the safety and health of home office lighting conditions are discussed. As COVID-19 continues to encourage remote working, individuals will be exposed to daylight and screen light in different ways compared to a traditional office environment. The impact of these different lighting conditions should be further studied to understand best design and well-being practices.

Structure of the stress-related LHCSR1 complex determined by an integrated computational strategy

Light-harvesting complexes (LHCs) are pigment-protein complexes whose main function is to capture sunlight and transfer the energy to reaction centers of photosystems. In response to varying light conditions, LH complexes also play photoregulation and photoprotection roles. In algae and mosses, a sub-family of LHCs, light-harvesting complex stress-related (LHCSR), is responsible for photoprotective quenching. Despite their functional and evolutionary importance, no direct structural information on LHCSRs is available that can explain their unique properties. In this work, we propose a structural model of LHCSR1 from the moss P. patens, obtained through an integrated computational strategy that combines homology modeling, molecular dynamics, and multiscale quantum chemical calculations. The model is validated by reproducing the spectral properties of LHCSR1. Our model reveals the structural specificity of LHCSR1, as compared with the CP29 LH complex, and poses the basis for understanding photoprotective quenching in mosses.

Intelligent Road Tracking and Real-time Acceleration-deceleration for Autonomous Driving Using Modified Convolutional Neural Networks

Neural network is one of the most widely used method in autonomous driving. Current researchers use only steering angle to train artificial neural networks, ignoring the importance of acceleration and deceleration for autonomous vehicles. We used an intelligent driving platform built with the Raspberry Pi 4 Model B, a front wide-angle camera, and a 1:16 scale model car to achieve real-time acceleration and deceleration while performing road tracking. Existing models cannot learn steering angle and throttle values well. This research proposed a novel architecture CNN model (PBLM-CNN21) to achieve real-time acceleration and deceleration while achieving road tracking. The PBLM-CNN21 model can learn steering angle and throttle value. The training loss value of our proposed PBLM-CNN21 model was 35% lower than the current TDD model, and the stability of our proposed road tracking model was 82% greater than that of the current TDD model. Furthermore, we tested the impact of different hyper-parameters on training model loss and road tracking performance. In addition, we also tested the effectiveness of varying lighting conditions and speed ratios on road tracking performance. The PBLM-CNN21 model proved more robust than the existing TDD models. Moreover, the PBLM-CNN21 model achieved road tracking under different lighting conditions and was more suitable for high-speed ratios.

ChickTrack – A quantitative tracking tool for measuring chicken activity

The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming.

Effect of stomatal morphology on leaf photosynthetic induction under fluctuating light across diploid and tetraploid rice

Polyploid plants are widely used in crop breeding for their superior performance in anatomical size and nutrient content. Photosynthesis is often affected by highly variable light conditions in the field. So far, few studies have been focused on the comparison of photosynthetic induction between tetraploid and diploid rice. Here, we for the first time compared the leaf photosynthetic induction and the underlying mechanism between tetraploid and diploid rice. After polyploidization, the plants displayed a larger size and lower density of stomata, which resulted in slower stomatal opening in response to a stepwise increase in light intensity. Further analysis indicated that stomatal opening, instead of biochemical processes, is the most important factor that contributes to the difference in photosynthetic induction between tetraploid and diploid rice. With lower stomatal conductance (gs), tetraploid rice displayed a lower intercellular CO2 concentration (Ci), and therefore lower photosynthetic efficiency during light induction. However, we observed a higher intrinsic water use efficiency (WUEi) in tetraploid rice than in diploid rice, which may contribute to the survival and growth of plants under water-limiting conditions. This study highlights the function of stomata in regulating photosynthetic induction and water use efficiency under fluctuating light, which will help the improvement of crop photosynthetic efficiency in the future.

TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots

Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.

Faster than expected Rubisco deactivation in shade reduces cowpea photosynthetic potential in variable light conditions

Cowpea is the major source of vegetable protein for rural populations in sub-Saharan Africa and average yields are not keeping pace with population growth. Each day, crop leaves experience many shade events and the speed of photosynthetic adjustment to this dynamic environment strongly affects daily carbon gain. Rubisco activity is particularly important because it depends on the speed and extent of deactivation in shade and recovers slowly on return to sun. Here, direct biochemical measurements showed a much faster rate of Rubisco deactivation in cowpea than prior estimates inferred from dynamics of leaf gas exchange in other species1–3. Shade-induced deactivation was driven by decarbamylation, and half-times for both deactivation in shade and activation in saturating light were shorter than estimates from gas exchange (≤53% and 79%, respectively). Incorporating these half-times into a model of diurnal canopy photosynthesis predicted a 21% diurnal loss of productivity and suggests slowing Rubisco deactivation during shade is an unexploited opportunity for improving crop productivity.