Search Results (16)

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

Plastic waste (PW) presents a significant environmental challenge due to its persistent accumulation and harmful effects on ecosystems. According to the United Nations Environment Program (UNEP), global plastic production in 2024 is estimated to reach approximately 500 million tons. Without effective intervention, most of this plastic is expected to become waste, potentially resulting in billions of tons of accumulated PW by 2060. This study explores innovative approaches to convert PW into high-value carbon nanomaterials (CNMs) such as graphene, carbon nanotubes (CNTs), and other advanced carbon structures. Various methods including pyrolysis, arc discharge, catalytic degradation, and laser ablation have been investigated in transforming PW into CNMs. However, four primary methodologies are discussed herein: thermal decomposition, chemical vapor deposition (CVD), flash joule heating (FJH), and stepwise conversion. The scalability of the pathways discussed for industrial applications varies significantly. Thermal decomposition, particularly pyrolysis, is highly scalable due to its straightforward setup and cost-effective operation, making it suitable for large-scale waste processing plants. It also produces fuel byproducts that can be used as an alternative energy source, promoting the concept of energy recovery and circular economy. CVD, while producing high-quality carbon materials, is less scalable due to the high cost and required complex equipment, catalyst, high temperature, and pressure, which limits its use to specialized applications. FJH offers rapid synthesis of high-quality graphene using an economically viable technique that can also generate valuable products such as green hydrogen, carbon oligomers, and light hydrocarbons. However, it still requires optimization for industrial throughput. Stepwise conversion, involving multiple stages, can be challenging to scale due to higher operational complexity and cost, but it offers precise control over material properties for niche applications. This research demonstrates the growing potential of upcycling PW into valuable materials that align with global sustainability goals including industry, innovation, and infrastructure (Goal 9), sustainable cities and communities (Goal 11), and responsible consumption and production (Goal 12). The findings underscore the need for enhanced recycling infrastructure and policy frameworks to support the shift toward a circular economy and mitigate the global plastic crisis. Full article

The surge in the global demand for plant-based proteins has catapulted pulse protein into the spotlight. To ensure economic viability and sustainable production, it is crucial to utilize pulse starch, a by-product of plant protein fractionation. Despite the increasing interest in pulse starches, there is a notable gap in knowledge regarding their modifications and applications compared to cereal and tuber starches. Non-thermal techniques such as electron beam radiation, static high pressure, microfluidization, and cold plasma are emerging as innovative methods for starch modification. These techniques offer significant advantages, including enhanced safety, environmental sustainability, and the development of unique functional properties unattainable through conventional methods. However, challenges such as equipment availability, high costs, and energy consumption hinder their widespread adoption. In light of the growing emphasis on “clean and green labelling” and effective “waste management” in food production, evaluating non-thermal techniques for pulse starch modification is critical. This review aims to thoroughly assess these non-thermal techniques and their combinations, offering valuable insights for researchers and the food industry. By maximizing the potential of pulse starches in innovative food applications, it provides a comprehensive guide for effective non-thermal methods that add value and align with sustainable practices. Full article

Microgreens are innovative vegetable products whose production and consumption are gaining popularity globally thanks to their recognized nutraceutical properties. To date, the effects of lighting conditions and growing substrate on the performances of Brassica carinata microgreens (indigenous to Africa) remain underexplored. The present study aimed at providing insights into the influence of different lighting treatments provided by LEDs, namely monochromatic blue (B), red (R), cool white (W) and a combination of three color diodes (B + R + W), and substrates (cocopeat, sand and cocopeat–sand mix (v/v) (1:1)) on the growth, yield and bioactive compounds of B. carinata microgreens. Seeds were germinated in dark chambers and cultivated in growth chambers equipped with LED lighting systems for 14 days under a fixed light intensity of 160 ± 2.5 µmol m⁻² s⁻¹ and photoperiod of 12 h d⁻¹. The best performances were associated with the spectrum that combined B + R + W LEDs and with substrate resulting from the cocopeat–sand mix, including the highest yield (19.19 g plant⁻¹), plant height (9.94 cm), leaf area (68.11 mm²) and canopy cover (55.9%). Enhanced carotenoid and flavonoid contents were obtained with B + R + W LEDs, while the B LED increased the total amount of chlorophyll (11,880 mg kg⁻¹). For plants grown under B + R + W LEDs in cocopeat, high nitrate levels were observed. Our results demonstrate that substrate and light environment interact to influence the growth, yield and concentration of bioactive compounds of B. carinata microgreens. Full article

A plant factory equipped with artificial lights is a comparatively new concept when growing seed potatoes (Solanum tuberosum L.) for minituber production. The shortage of disease-free potato seed tubers is a key challenge to producing quality potatoes. Quality seed tuber production all year round in a controlled environment under an artificial light condition was the main purpose of this study. The present study was conducted in a plant factory to investigate the effects of distinct spectrum compositions of LEDs on potato tuberization when grown in an aeroponic system. The study was equipped with eight LED light combinations: L1 = red: blue: green (70 + 25 + 5), L2 = red: blue: green (70 + 20 + 10), L3 = red: blue: green (70 + 15 + 15), L4 = red: blue: green (70 + 10 + 20), L5 = red: blue: far-red (70 + 25 + 5), L6 = red: blue: far-red (70 + 20 + 10), L7 = red: blue: far-red (70 + 15 + 15), L8 = red: blue: far-red (70 + 10 + 20), and L9 = natural light with 300 µmol m⁻² s⁻¹ of irradiance, 16/8 h day/night, 65% relative humidity, while natural light was used as the control treatment. According to the findings, treatment L4 recorded a higher tuber number (31/plant), tuber size (>3 g); (9.26 ± 3.01), and GA₃ content, along with better plant growth characteristics. Moreover, treatment L4 recorded a significantly increased trend in the stem diameter (11.08 ± 0.25), leaf number (25.32 ± 1.2), leaf width (19 ± 0.81), root length (49 ± 2.1), and stolon length (49.62 ± 2.05) compared to the control (L9). However, the L9 treatment showed the best performance in plant fresh weight (67.16 ± 4.06 g) and plant dry weight (4.46 ± 0.08 g). In addition, photosynthetic pigments (Chl a) (0.096 ± 0.00 mg g⁻¹, 0.093 ± 0.00 mg g⁻¹) were found to be the highest in the L1 and L2 treatments, respectively. However, Chl b and TCL recorded the best results in treatment L4. Finally, with consideration of the plant growth and tuber yield performance, treatment L4 was found to have the best spectral composition to grow quality seed potato tubers. Full article

Ensuring the technical and technological possibility of regularly obtaining fresh, high-quality plant production in Antarctic stations is an urgent task of our time. This work is devoted to studying the growth and development of leaf vegetable crops and the main quality indicators of their edible parts when grown in the phytotechnical complex greenhouses at the “Vostok” Antarctic station and at the agrobiopolygon of the Agrophysical Research Institute (AFI). The plants, belonging to 13 varieties of 9 types of leaf vegetable crops (arugula, garden cress, cabbage, mustard, leaf radish, leaf lettuce, amaranth, dill, parsley leaf), were studied during five growing seasons at the “Vostok” station and at the AFI agrobiopolygon under controlled conditions (control). The experimental data obtained demonstrate the high productivity of the phytotechnical complex for most of the investigated crops per unit of useful area, with lower costs of electricity and water consumption per unit of production compared with a number of greenhouses at foreign Antarctic stations and greenhouse complexes with controlled conditions located on other continents. Lettuce crops were the most adapted to the growing conditions at the Antarctic station “Vostok”. They did not differ in their evaluated characteristics from the control. All other investigated crops, while not differing in their development rate and quality, had statistically significant (16–61%) decreases in their yield per 1 m² per year. This may demonstrate the difference in the “genotype–environment” interaction in plants grown at the Antarctic station and AFI agrobiopolygon, probably due to the different barometric pressure and partial pressure of oxygen at the two locations. The positive psychological effects of the greenhouses were identified along with nutritional and other qualities of the plants. Full article

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is destructive requiring manual labor and may cause damages for the plants. In this work, we report on the AI-based approach for assessing and predicting the leaf area and plant biomass. The proposed approach is able to estimate and predict the overall plants biomass at the early stage of growth in a non-destructive way. For this reason we equip an industrial greenhouse for cucumbers growing with the commercial off-the-shelf environmental sensors and video cameras. The data from sensors are used to monitor the environmental conditions in the greenhouse while the top-down images are used for training Fully Convolutional Neural Networks (FCNN). The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy. Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics. Then we established the dependency between the average leaf area and biomass using the direct measurements of the biomass. This in turn allowed for reconstruction and prediction of the dynamics of biomass growth in the greenhouse using the image data with 10% average relative error for the 12 days prediction horizon. The actual deployment showed the high potential of the proposed data-driven approaches for plant growth dynamics assessment and prediction. Moreover, it closes the gap towards constructing fully closed autonomous greenhouses for harvests and plants biological safety. Full article

The food industry is a high consumer of polymer packing materials, sealing materials, and engineering components used in production equipment. Biobased polymer composites used in the food industry are obtained by incorporating different biogenic materials into the structure of a base polymer matrix. Renewable resources such as microalgae, bacteria, and plants may be used as biogenic materials for this purpose. Photoautotrophic microalgae are valuable microorganisms that are able to harvest sunlight energy and capture CO₂ into biomass. They are characterized by their metabolic adaptability to environmental conditions, higher photosynthetic efficiency than terrestrial plants, and natural macromolecules and pigments. The flexibility of microalgae to grow in either low-nutrient or nutrient-rich environments (including wastewater) has led to the attention for their use in various biotechnological applications. Carbohydrates, proteins, and lipids are the main three classes of macromolecular compounds contained in microalgal biomass. The content in each of these components depends on their growth conditions. In general, proteins represent 40–70% of microalgae dry biomass, followed by carbohydrates (10–30%) and lipids (5–20%). A distinctive feature of microalgae cells is the presence of light-harvesting compounds such as photosynthetic pigments carotenoids, chlorophylls, and phycobilins, which are also receiving growing interest for applications in various industrial fields. The study comparatively reports on polymer composites obtained with biomass made of two species of green microalgae: Chlorella vulgaris and filamentous, gram-negative cyanobacterium Arthrospira. Experiments were conducted to reach an incorporation ratio of the biogenic material into the matrix in the 5–30% range, and the resulting materials were characterized by their mechanical and physicochemical properties. Full article

Vertical farming (VF) is a newer crop production practice that is attracting attention from all around the world. VF is defined as growing indoor crops on multiple layers, either on the same floor or on multiple stories. Most VF operations are located in urban environments, substantially reducing the distance between producer and consumer. Some people claim that VF is the beginning of a new era in controlled environment agriculture, with the potential to substantially increase resource-use efficiencies. However, since most vertical farms exclusively use electric lighting to grow crops, the energy input for VF is typically very high. Additional challenges include finding and converting growing space, constructing growing systems, maintaining equipment, selecting suitable plant species, maintaining a disease- and pest-free environment, attracting and training workers, optimizing the control of environmental parameters, managing data-driven decision making, and marketing. The objective of the paper is to highlight several of the challenges and issues associated with planning and operating a successful vertical farm. Industry-specific information and knowledge will help investors and growers make informed decisions about financing and operating a vertical farm. Full article

Environmental changes and the reduction in arable land have led to food security concerns around the world, particularly in urban settings. Hydroponic soilless growing methods deliver plant nutrients using water, conserving resources and can be constructed nearly anywhere. Hydroponic systems have several complex attributes that need to be managed, and this can be daunting for the layperson. Micro Indoor Smart Hydroponics (MISH) leverage Internet of Things (IoT) technology to manage the complexities of hydroponic techniques, for growing food at home for everyday citizens. Two prohibitive costs in the advancement of MISH systems are power consumption and equipment expense. Reducing cost through harvesting ambient light can potentially reduce power consumption but must be done accurately to sustain sufficient plant yields. Photosynthetic Active Radiation (PAR) meters are commercially used to measure only the light spectrum that plants use, but are expensive. This study presents Adaptalight, a MISH system that harvests ambient light using an inexpensive AS7265x IoT sensor to measure PAR. The system is built on commonly found IoT technology and a well-established architecture for MISH systems. Adpatalight was deployed in a real-world application in the living space of an apartment and experiments were carried out accordingly. A two-phase experiment was conducted over three months, each phase lasting 21 days. Phase one measured the IoT sensor’s capability to accurately measure PAR. Phase two measured the ability of the system to harvest ambient PAR light and produce sufficient yields, using the calibrated IoT sensor from phase one. The results showed that the Adaptalight system was successful in saving a significant amount of power, harvesting ambient PAR light and producing yields with no significant differences from the control. The amount of power savings would be potentially greater in a location with more ambient light. Additionally, the findings show that, when calibrated, the AS7265x sensor is well suited to accurately measure PAR light in MISH systems. Full article

The concept of circular economy in wastewater treatment has recently attracted immense interest and this is primarily fueled by the ever-growing interest to minimise ecological footprints of mining activities and metallurgical processes. In light of that, countries such as the Republic of South Africa, China, Australia, and the United States are at the forefront of water pollution due to the generation of notorious acid mine drainage (AMD). The disposal of AMD to different receiving environments constitutes a severe threat to the receiving ecosystem thus calling for prudent intervention to redress the prevailing challenges. Recent research emphasises the employment of wastewater treatment, beneficiation and valorisation. Herein, the techno-economic evaluation of the reclamation of clean water and valuable minerals from AMD using the Magnesite Softening and Reverse Osmosis (MASRO) process was reported. The total capital expenditure (CAPEX) for the plant is ZAR 452,000 (USD 31,103.22) which includes ZAR 110,000 (USD 7569.37) for civil works on a plant area of 100 m². The operational expenditure (OPEX) for the pilot is 16,550,000 ZAR (South African Rand) or USD 1,138,845.72 in present value terms (10 years plant life). The plant reclaimed drinking water as specified in different water quality standards, guidelines, and specifications, including Fe-based minerals (goethite, magnetite, and hematite), Mg-gypsum, and calcium carbonate. These minerals were verified using state-of-the-art analytical equipment. The recovered valuables will be sold at ZAR 368/kL (USD 25.32), ZAR 1100/t (USD 75.69), and ZAR 2000/t (USD 137.62) for water, gypsum, and limestone, respectively. The project has an NPV of ZAR 60,000 (USD 4128.75) at an IRR of 26%. The payback period for this investment will take 3 years. The total power consumption per day was recorded to be 146.6 kWh, and 103,288 kWh/annum. In conclusion, findings of this work will significantly contribute to improving the sustainability of the mining sector by proposing economically feasible solutions for wastewater streams treatment, beneficiation, and valorisation. Full article

The microgreens are innovative products in the horticultural sector. They are appreciated by consumers thanks to their novelty and health-related benefits, having a high antioxidant concentration. This produce can be adopted for indoor production using hydroponic systems. The aim of the present work was to investigate the influence of three growing media (vermiculite, coconut fiber, and jute fabric) on yield and quality parameters of two basil varieties (Green basil—Ocimum basilicum L., Red basil—Ocimum basilicum var. Purpurecsens) and rocket (Eruca sativa Mill.) as microgreens. Microgreens were grown in floating, in a Micro Experimental Growing (MEG^®) system equipped with LED lamps, with modulation of both energy and spectra of the light supplied to plants. Results showed high yield, comprised from 2 to 3 kg m⁻². Nutritional quality varied among species and higher antioxidant compounds were found in red basil on vermiculite and jute. Coconut fiber allowed the differentiation of crop performance in terms of sucrose and above all nitrate. In particular, our results point out that the choice of the substrate significantly affected the yield, the dry matter percentage and the nitrate concentration of microgreens, while the other qualitative parameters were most influenced by the species. Full article

Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R² = 0.71 at the row level and R² = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs. Full article

The applicability of organic Rankine cycle (ORC) technology to waste heat recovery (WHR) is currently experiencing growing interest and accelerated technological development. The utilization of low-to-medium grade thermal energy sources, especially in the presence of heat source intermittency in applications where the thermal source is characterized by highly variable thermodynamic conditions, requires a control strategy for off-design operation to achieve optimal ORC power-unit performance. This paper presents a validated comprehensive model for off-design analysis of an ORC power-unit, with R236fa as the working fluid, a gear pump, and a 1.5 kW sliding vane rotary expander (SVRE) for WHR from the exhaust gases of a light-duty internal combustion engine. Model validation is performed using data from an extensive experimental campaign on both the rotary equipment (pump, expander) and the remainder components of the plant, namely the heat recovery vapor generator (HRVH), condenser, reservoirs, and piping. Based on the validated computational platform, the benefits on the ORC plant net power output and efficiency of either a variable permeability expander or of sliding vane rotary pump optimization are assessed. The novelty introduced by this optimization strategy is that the evaluations are conducted by a numerical model, which reproduces the real features of the ORC plant. This approach ensures an analysis of the whole system both from a plant and cycle point of view, catching some real aspects that are otherwise undetectable. These optimization strategies are considered as a baseline ORC plant that suffers low expander efficiency (30%) and a large parasitic pumping power, with a backwork ratio (BWR) of up to 60%. It is found that the benefits on the expander power arising from a lower permeability combined with a lower energy demand by the pump (20% of BWR) for circulation of the working fluid allows a better recovery performance for the ORC plant with respect to the baseline case. Adopting the optimization strategies, the average efficiency and maximum generated power increase from 1.5% to 3.5% and from 400 to 1100 W, respectively. These performances are in accordance with the plant efficiencies found in the experimental works in the literature, which vary between 1.6% and 6.5% for similar applications. Nonetheless, there is still room for improvement regarding a proper design of rotary machines, which can be redesigned considering the indications resulting from the developed optimization analysis. Full article

Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R²) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R² > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season. Full article