Search Results (55)

This experiment aimed to investigate nutrient dynamics in soil and compare plant growth responses after treatment with acidified biochar pellets inoculated with microorganisms during Kimchi cabbage cultivation, using a sigmoid function model. The treatments included the following: Control–only guano application; ABPM 27 (Pseudomonas fluorescens 22BCO027); and ABPM 86 (Bacillus megaterium 22BCO086). Guano and biochar pellets were applied at 320 kg ha⁻¹, based on the recommended nitrogen application rate for cabbage cultivation. The results showed that the cumulative NO₃-N and P₂O₅ in the ABPM 27 treatment were 27.7% and 12.1% higher, respectively, compared with the control. The maximum cumulative K was not significantly different (p > 0.05) between the treatments. The cumulative NH₄-N and NO₃-N were well fitted (R² > 0.824) to the sigmoid curves, while the cumulative P₂O₅ and K were well described with the linear function (R² > 0.970) regardless of treatment. The highest yield was 77.4 tonnes ha⁻¹ under the ABPM 27 treatment. Therefore, the ABPM 27 treatment is strongly recommended for enhancing cabbage yield in organic farming due to its high capacity for accumulating NO₃-N and P₂O₅. Full article

Background/Objectives: Taxonomic profiling of soil microbial communities is useful for assessing and monitoring the biological status of agricultural land. In this study, we aimed to investigate changes in the taxonomic structure of soil organisms in minimally managed agricultural fields. Methods: We used DNA metabarcoding to investigate both terrestrial prokaryotes and eukaryotes in cabbage-cultivated and uncultivated sites in a minimally managed agricultural field in central Japan from February to August 2021. Analyses of the relative abundances of prokaryotic and eukaryotic sequence variants (SVs) and their β-diversities, and the subsequent redundancy analysis (RDA) clarified the dynamic changes in eukaryotic communities during cultivation. We further investigated taxonomic changes in fungi-, protist-, and animal-derived SVs, abundant SVs in each eukaryotic phylum, as well as the co-occurrence networks of the top 150 SVs. Results: The results revealed that the fractions of predatory or parasitic protists and animals increased, whereas those of fungi and earthworm Enchytraeus spp. decreased. The fractions of abundant SVs derived from diatoms, Ciliophora, the class Vampyrellidae (Cercozoa), and mites increased and subsequently decreased during this period. These findings suggest that predatory protists and animals fed on bacteria and autotrophic eukaryotes (such as diatoms) propagated in spring, followed by their propagation and parasitism to host eukaryotes. The networks also changed, especially prokaryotic networks that markedly changed from April to May, and those of eukaryotes from May to June–August, supporting the observations mentioned above. Conclusions: These findings indicate the dynamic and sequential changes in soil communities in fields with minimal agricultural practices and could be useful for sustainable natural farming. Full article

South African farming households face several challenges regarding food security, poverty, micronutrient deficiencies and hidden hunger. This is due to millions of households lacking access to food and an adequate food basket. Consumption of indigenous crops has been proposed to help sustain vulnerable households since these crops have low production costs and are climate-resilient. However, research has found the consumption of these crops across South Africa to be relatively low. This study aims to examine the factors associated with the consumption of indigenous crops among farming households in the KwaZulu-Natal province of South Africa. A sample of 260 farming households was selected using simple random sampling. The results showed that farmers commonly cultivate exotic crops, such as spinach, cabbage, carrot, and butternut, more than any indigenous crops, except for a few cultivating taro and sweet potato. The binomial logit regression results revealed that an increase in the number of females and children within a household and farmers’ experience increased the likelihood of consuming indigenous crops, whilst monthly food expenses decreased the likelihood of consuming indigenous crops. There is a considerable gap between the consumption and production of indigenous crops. The findings established that although many farming households indicated that they consume indigenous crops, this was not reflected in their cultivated crops. The study also concluded that farming households may be more aware of the nutritional benefits of indigenous crops, since an increase in the number of children in a household was linked to an increase in consumption of these crops. Additionally, experience in farming is vital, as it increases the consumption of indigenous crops. The study recommends government interventions that include increasing the production of indigenous crops by including them alongside the cultivation of exotic crops. Future work should also focus on awareness programs to promote the nutritional benefits of consuming indigenous crops. This, coupled with training centered on indigenous crops, could incentivize farming households to cultivate more of these crops for easier access. Full article

Under the increasing severity of drought issues and the urgent need for the resourceful utilization of agricultural waste, this study aimed to compare the soil water retention properties of hydrogels prepared from Chinese cabbage waste (CW) and banana peel (BP) using grafting techniques with acrylic acid (AA) and acrylamide (AAm). Free radical polymerization was initiated with ammonium persulfate (APS), and N, N′-methylene bisacrylamide (MBA) served as the crosslinking agent to fabricate the grafted polymer hydrogels. The hydrogels were subjected to detailed evaluations of their water absorption, reusability, and water retention capabilities through indoor experiments. The optimal hydrogel was identified and its applicability in wheat seedling growth was assessed. The findings revealed that the CW-gel, with an equilibrium swelling ratio of 551.8 g/g in ultrapure water, demonstrated remarkable performance and sustained a high water retention of 57.6% even after drying, which was markedly superior to that of the BP-gel. The CW-gel with the best comprehensive properties significantly improved water retention in sandy soil by 78.2% and prolonged the retention time by five days, indicating its potential for long-term irrigation management. In contrast, the BP-gel showed better performance in clay soil, with an increased water-holding capacity of 43.3%. The application of a 1.5% CW-gel concentration under drought stress significantly improved wheat seedling growth, highlighting the role of hydrogels in agriculture and providing a new path for sustainable water resource management in dryland farming. Full article

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as “Jordan22” was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. Full article

Organic mulching is a promising technique for sustainable weed control and soil management, as it enhances crop growth, soil quality, water retention, and erosion control. This research evaluated the effects of organic mulches—wheat straw, wood chips, spray cellulose pulp, compost, and a cover crop mixture—on the physical–mechanical properties of organic garden soil transitioning to natural farming. The controlled soil received no mulch. The soil was fertilized with mature bovine manure prior to a three-year crop rotation of tomato, lettuce, and savoy cabbage. Mulching occurred after the second harrowing and before transplanting. Soil analyses were conducted to assess changes after three years. Soil organic carbon levels increased significantly in soils treated with compost, cover crops, or chipped wood mulching (6.81, 3.17, and 2.07%, respectively) compared to other treatments (1.24% in the control plot). Different kinds of mulch had a significant impact on soil’s physical–mechanical parameters. Compost, compared to the control, decreased the bulk density (from 1.22 to 0.89 Mg m⁻³), increased the infiltration rate (from 8.53 to 21.07 L m⁻²), and reduced compressive deformation (from 37.08 to 18.23%). The composition of mulch materials, specifically their nitrogen and carbon concentrations, C/N ratio, and moisture content, plays a significant role in influencing changes in soil properties. Full article

Accurately detecting the maturity and 3D position of flowering Chinese cabbage (Brassica rapa var. chinensis) in natural environments is vital for autonomous robot harvesting in unstructured farms. The challenge lies in dense planting, small flower buds, similar colors and occlusions. This study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields. In this study, C2F-MLCA is created by adding a lightweight Mixed Local Channel Attention (MLCA) with spatial awareness capability to the C2F module of YOLOv8, which improves the extraction of spatial feature information in the backbone network. In addition, a P2 detection layer is added to the neck network, and BiFPN is used instead of PAN to enhance multi-scale feature fusion and small target detection. Wise-IoU in combination with Inner-IoU is adopted as a new loss function to optimize the network for different quality samples and different size bounding boxes. Lastly, ByteTrack is integrated for video tracking, and RGB-D camera depth data are used to estimate cabbage positions. The experimental results show that YOLOv8-Improve achieves a precision (P) of 86.5% and a recall (R) of 86.0% in detecting the maturity of flowering Chinese cabbage. Among them, mAP50 and mAP75 reach 91.8% and 61.6%, respectively, representing an improvement of 2.9% and 4.7% over the original network. Additionally, the number of parameters is reduced by 25.43%. In summary, the improved YOLOv8 algorithm demonstrates high robustness and real-time detection performance, thereby providing strong technical support for automated harvesting management. Full article

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R² = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization. Full article

The cabbage whitefly has become an important pest on brassica vegetables in Central Europe. It does not destroy the affected plants, but the product becomes unmarketable, causing considerable economic losses. The pest is also difficult to control due to its way of life and because it develops resistance to some of the active components of insecticides. In organic farming systems, insecticides are strictly restricted, but neither predators nor whitefly parasitoids are able to keep the pest at a tolerable level. It is, therefore, necessary to become familiar with the whitefly’s life cycle and habits, including mass migration from winter hosts to vegetables. We inspected 44 rapeseed fields across the republic in the period 2014–2021 in order to find the connection between the presence of oilseed rape fields near vegetable growing areas (VGAs) and the abundance of the overwintering cabbage whiteflies. We also conducted regular weekly monitoring of whitefly occurrence in the main cultivation area of the Czech Republic (Polabí) with the aim of specifying critical data important for the successful control of this pest. We found that the cabbage whitefly incidences were many times higher in rapeseed fields close to VGAs compared to areas where the crops are not adjacent. The average number of whiteflies was 0.59 individuals per plant in VGA-1 (oilseed rape grown inside this area or up to 1 km far), 0.052 in VGA-2 (distance 3–10 km from vegetable fields) and 0.014 in VGA-3 (more than 20 km). In the extremely warm year 2016, the difference was up to sixty times. The first CW eggs laid on cruciferous vegetables were usually found around 20 May. The period of mass migration of CW adults to cruciferous vegetables was between 6 June and 2 August. At this time, vegetables are most vulnerable to damage. Successful control of the cabbage whitefly requires the use of fabric netting, combined with an insecticide as needed and trap plants as needed; the latter have to be destroyed before adult whiteflies hatch—typically in early July. Full article

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring. Full article

Inefficiency is a problem in the production process, including in the organic farming sector. Over a long term period, this problem can disrupt the productivity of agricultural crops. This research aims to analyze the production and marketing efficiency of organic cabbage farming in the Kopeng agropolitan area, Indonesia. We utilized a Cobb–Douglas production efficiency analysis with the Stochastic Frontier Analysis (SFA) approach. The variables in this study include organic cabbage production, land area, seedlings, organic fertilizers, organic pesticides, and labor. We conducted in-depth interviews with 60 organic cabbage farmers in Kopeng, Indonesia, from January to August 2023. The research results showed that organic cabbage cultivation was economically inefficient in production, technical, and marketing. The use of organic fertilizers, the ability to diversify products on limited land, and the use of pesticides, have not been utilized optimally yet. The results of the marketing efficiency analysis showed that it was efficient. Organic plants were believed to have their market share and to have a higher selling value than non-organic ones. The implication was that the government needed to provide training in producing organic fertilizers and pesticides to reduce production costs so that organic farming could be technically and financially efficient. This research enriched the discussion regarding the need to analyze production and marketing efficiency to find strategies to increase organic cabbage productivity. Full article

The rising adoption of agricultural technologies such as the Internet of Things (IoT) or “smart farming” aims to boost crop production in terms of both quantity and quality. This study compares the benefits of a smart farm employing an IoT-based hydroponic system with those of a conventional hydroponic farm, using Chinese cabbage (Brassica pekinensis L.) as the experimental crop. Our primary objective was to automate environmental monitoring, achieving pH level and electrical conductivity (EC) maintenance through smartphone or computer interfaces for nutrient and acid–base solution adjustments. Additionally, we evaluated plant growth and crop quality, finding superior results with the smart hydroponic system. On average, there were substantial increases in various parameters, including total fresh weight (27.14%), total dry weight (48.90%), plant height (11.14%), stem diameter (32.89%), leaf area (94.30%), leaf width (32.36%), leaf length (38.12%), and chlorophyll content (22.73%). Nitrate accumulation in the edible parts of Chinese cabbage remained within safe limits for both systems, reflecting careful nutrient management. These findings highlight the potential of IoT-based technology in enhancing productivity and quality in hydroponic farming, marking a significant step towards revolutionizing traditional agricultural practices for more efficient crop production systems. Full article

Cabbage is an annual vegetable crop species cultivated throughout the year. The development of high-yielding cabbage hybrids and the optimization of several agronomic management practices such as fertilization and crop rotation have resulted in increased soil fertility, crop yield and product quality. This study aimed to investigate the effects of the farming system (organic and conventional) and the applied rotation scheme on soil nutrient content, head yield and the nutrient content of cabbage. The preceding crops included either pea (P), faba bean (F) or cabbage (C), and thus, the rotation schemes were P-C, F-C and C-C. Sheep manure was applied in the organic farming system, and the inorganic fertilizer 11-15-15 (N-P₂O₅-K₂O) was applied to the conventionally cultivated plants. The results reveal an interaction between the farming system and the preceding crop for the head yield, with the lowest values (57.00 t ha⁻¹ and 53.87 t ha⁻¹ in 2015/2016 and 2016/2017, respectively) recorded in plots where cabbage was cultivated as a preceding crop under the organic farming system. The N, P and K contents in head tissues were affected only by the farming system, with the greatest values recorded in the conventional farming system. Both factors affected the nutrient content in the soil. Specifically, the highest values of NO₃⁻ and total N content in the soil were recorded in the P-C and F-C rotations, and the K content was higher in the continuous cabbage cropping system (C-C). Moreover, the NO₃⁻, P and K contents in the soils were higher in the conventional farming system compared to the organic system. To conclude, combining inorganic fertilization in a crop rotation scheme with legume species such as pea and faba bean as preceding crops for cabbage can result in increased soil fertility and head yield. Full article

Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines. Full article

Agrivoltaic systems, which combine the cultivation of crops with solar panel installations, offer a novel solution to the dual challenges of energy production and agricultural productivity. This research verifies the impact of agrivoltaic (APV) conditions on the growth and quality of garlic and kimchi cabbage over two consecutive years in Naju-si, Jeollanam Province, Republic of Korea. In the 2019–2020 cultivation season, both kimchi cabbage and garlic grown under APV conditions experienced weight reductions of 18% and 15%, respectively, when compared to those grown in conventional settings. Intriguingly, despite the altered light conditions of APV leading to microenvironmental changes (mainly 41% light reduction), the quality of these crops, particularly in terms of their sulfur compound concentrations, remained consistent. This suggests that there was no discernible difference in the sensory quality of APV-grown kimchi cabbage and garlic compared to their traditionally grown counterparts. These findings highlight the potential of APV systems in promoting sustainable agriculture by balancing both crop yield and quality. Based on these results, the study suggests three innovative cultivation techniques to enhance crop growth in APV environments. Full article