Search Results (90)

This study investigates how cover crop management and soil tillage influence the development and yield of cucumber and cabbage crops. Three cover crop treatments—blue lupin, black oats, and their mixture—were evaluated during the autumn/winter season, while Stylosanthes capitata (Fabaceae), pearl millet (Pennisetum glaucum, Poaceae), and their mixture were assessed during the spring/summer season, under both conventional tillage and no-till (direct seeding) systems. Cover crops were established in spring/summer (October–November) and, after their management, cucumber (Cucumis sativus L.) was cultivated from December to February. Subsequently, winter cover crops were grown from May to July, followed by cabbage (Brassica oleracea var. capitata) cultivation from July to September. Drip irrigation was used, and organic practices were employed for weed, pest, and disease management. Germination, seedling survival rate, and plant growth (height, number of leaves, foliage cover, and fruit or cabbage size) were evaluated. Finally, crop yield is considered by comparing harvest weight and quality to determine which combination of soil cover and planting method maximizes crop productivity and quality. Obviously, management differences that influence yield will be associated with soil properties. To better understand the causes of these yield differences, the influence of soil chemical properties was explored using multivariate analysis techniques (discriminant analysis) and neural networks. Multivariate techniques allow for the exploration of complex relationships among multiple variables simultaneously, facilitating the identification of key patterns or factors that influence crop yield. On the other hand, neural networks, using machine learning models, allow for the prediction of outcomes based on the soil’s physicochemical properties, as well as the identification of optimal combinations of factors that maximize crop yield. Discriminant analysis and neural networks showed that soil variables such as pH, organic matter (OM), cation exchange capacity (CEC), phosphorus (P), and potassium (K) were key determinants in differentiating the yield groups. Cabbage yield was most strongly associated with pH and OM, while cucumber yield responded more strongly to potassium and CEC. Full article

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

New Zealand’s only tea (Camellia sinensis) plantation supplies a niche market for organically produced high value tea but faces challenges from climatic conditions and the decision to use only organic production methods. Fungi from the genus Trichoderma have been commercialised in New Zealand and elsewhere as disease-suppressing and plant growth-promoting agents. However, the potential benefits of using Trichoderma as a microbial biostimulant for tea cultivation have not been investigated in New Zealand. The ability of T. atroviride application to stimulate tea plant growth at a tea plantation was investigated over one year of production. The study involved foliar application of the biostimulant either once, twice or three times, one month apart, using 12 g of a commercially formulated spore mixture of four strains of T. atroviride per 5 m² of experimental plots. Treatment with T. atroviride significantly increased tea yield by between 17% and 28% compared to the control over the harvesting season, but there were no statistically significant yield differences among the number of applications. The foliar applied T. atroviride was not detected in the soil or root samples six months after application, in either a soil metabarcoding analysis or on re-isolation media. This was likely due to the dense tea foliage and ground cover under the tea plants which impeded its movement to the soil. While the specific nature of T. atroviride interaction with perennial crops like tea is not known, in this trial it appeared to have remained on the phyllosphere and provided biostimulation without reaching the soil. Full article

Air pollution, soil contamination, and rising illness demand integrated, nature-based solutions. Willow trees (Salix spp.) uniquely combine ecological resilience with therapeutic value, remediating polluted environments while supporting human well-being. This review synthesizes recent literature on the established role of Salix spp. in phytoremediation and growing contribution to forest therapy through emissions of biogenic volatile organic compounds (BVOCs). As urbanization accelerates and environmental pressures intensify globally, the surprising adaptability and multifunctionality of Salix justify the utilization of this genus in building resilient and health-promoting ecosystems. The major points discussed in this work include willow-based phytoremediation strategies, such as rhizodegradation, phytoextraction, and phytostabilization, contributing to restoring even heavily polluted soils, especially when combined with specific strategies of microbial augmentation and trait-based selection. Salix plantations and even individual willow trees may contribute to forest therapy (and ‘forest bathing’ approaches) through volatile compounds emitted by Salix spp. such as ocimene, β-caryophyllene, and others, which exhibit neuroprotective (against Parkinson’s disease), anti-inflammatory, and mood-enhancing properties. Willow’s significantly extended foliage season in temperate regions allows for prolonged ‘forest bathing’ opportunities, enhancing passive therapeutic engagement in urban green infrastructures. Remarkably, the pharmacological potential of willow extends beyond salicin, encompassing a diverse array of phytocompounds with applications in phytomedicine. Finally, willow’s ease of propagation and adaptability make this species a convenient solution for multifunctional landscape design, where ecological restoration and human well-being converge. Overall, this review demonstrates the integrative value of Salix spp. as a keystone genus in sustainable landscape planning, combining remarkable environmental resilience with therapeutic benefits. Future studies should explore standardized methods to evaluate the combined ecological and therapeutic performance of Salix spp., integrating long-term field monitoring with analyses of BVOC emissions under varying environmental stresses. Full article

Boxwood (Buxus sp.) plays a key role in historical gardens due to its evergreen foliage and resilience. However, recent outbreaks of disease caused by fungal pathogens such as Calonectria spp. (C. pseudonaviculata, C. henricotiae) and Pseudonectria spp. (P. buxi, P. foliicola), as well as pest pressures from Cydalima perspectalis, have led to significant losses. This study examined 100 boxwood plantings across the Czech Republic to evaluate pest and disease occurrence. Further, six modern boxwood cultivars from the groups of BetterBuxus^® and NewGen^® were tested in field trials under the climatic conditions of the Czech Republic, focusing on their resistance to abiotic stress and foliage color retention throughout the year. Laboratory trials confirmed all cultivars were susceptible to C. pseudonaviculata, with ‘Renaissance’ showing the slowest disease progression. Field assessments under two contrasting management regimes (“Minimalistic” and “Pampered”) indicated sporadic boxwood blight incidence but frequent Volutella blight outbreaks, particularly where plants suffered frost stress. Leaf color, an important esthetic trait, was evaluated using Munsell charts and measuring the relative chlorophyll content. ‘Skylight’ most closely matched Buxus sempervirens in the shade of green and winter color. Full article

Grapefruit (Citrus × paradisi Macfad.) is susceptible to Huanglongbing (HLB) disease, which prominently affects tree health and leads to a substantial loss of productivity. HLB-affected trees exhibit a nutritional imbalance expressed in either deficiencies or toxicities of the essential minerals required for plant growth, as well as changes in the production of plant metabolites. Hence, understanding foliar nutritional and metabolite fluctuations as HLB-elicited symptoms progress can assist growers in improving tree health management strategies. This study evaluated changes in foliar nutrient and phloem sap amino acid concentrations of HLB-affected grapefruit trees showing a mixed canopy of HLB-induced blotchy mottle and asymptomatic mature leaves. The trees used in our experiment were fruit-bearing seven-year-old grapefruit trees (cv ‘Rio Red’ on sour orange rootstock) grown in South Texas. Two types of foliage from HLB-affected trees were studied, (a) HLB-symptomatic and confirmed Candidatus Liberibacter asiaticus (CLas)-positive (IS) and (b) CLas-negative and HLB-asymptomatic (IA) mature leaves, which were compared to asymptomatic and CLas-free mature foliage from healthy trees (HY) in terms of their leaf nutrient and phloem sap amino acid contents. Hierarchical clustering based on leaf nutrient contents showed that 70% of IA samples clustered with HY samples, thus indicating that the levels of some nutrients were statistically similar in these two types of samples. The concentrations of the macronutrients N, Ca, Mg, and S and the micronutrients Mn and B were significantly reduced in HLB-symptomatic (IS) leaves, as compared to their IA and HY counterparts, which did not show statistically significant differences. Conversely, leaf Na concentration was approximately two-fold higher in leaves from HLB-affected trees (IA and IS) independent of symptom expression as compared to leaves from healthy trees. Significantly higher concentrations of glutamine and the S-containing amino acids taurine and cystathionine were observed in the IS leaves relative to the phloem sap of IA leaves from HLB-affected trees. In contrast, the phloem sap of IA (14%) and IS (41%) leaves from HLB-affected trees exhibited lower levels of γ-amino butyric acid (GABA) as compared to HY leaves. The results of this study highlight the changes in leaf nutrient and phloem sap amino acid profiles following CLas infection and HLB symptom development in grapefruit, and we discuss these results considering the strategies that growers can implement to correct the nutritional deficiencies and/or toxicities induced by this disease. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus Steiner & Buhrer (pine wood nematodes, PWN), impacts forest carbon sequestration and climate change. However, satellite-based PWD monitoring is challenging due to the limited spatial resolution of Sentinel’s MSI sensor, which reduces its sensitivity to subtle biochemical alterations in foliage. We have, therefore, developed a slope product index (SPI) for effective detection of PWD using single-date satellite imagery based on spectral gradients in the visible and near-infrared (VNIR) range. The SPI was compared against 15 widely used vegetation indices and demonstrated superior robustness across diverse test sites. Results show that the SPI is more sensitive to changes in chlorophyll content in the PWD detection, even under potentially confounding conditions such as drought. When integrated into Random Forest (RF) and Back-Propagation Neural Network (BPNN) models, SPI significantly improved classification accuracy, with the multivariate RF model achieving the highest performance and univariate with SPI in BPNN. The generalizability of SPI was validated across test sites in distinct climate zones, including Zhejiang (accuracyZ_Mean = 88.14%) and Shandong (accuracyS_Mean = 78.45%) provinces in China, as well as Portugal. Notably, SPI derived from Sentinel-2 imagery in October enables more accurate and timely PWD detection while reducing field investigation complexity and cost. Full article

This work proposes an end-to-end solution for leaf segmentation, disease detection, and damage quantification, specifically focusing on citrus crops. The primary motivation behind this research is to enable the early detection of phytosanitary problems, which directly impact the productivity and profitability of Spanish and Portuguese agricultural developments, while ensuring environmentally safe management practices. It integrates an onboard computing module for Unmanned Aerial Vehicles (UAVs) using a Raspberry Pi 4 with Global Positioning System (GPS) and camera modules, allowing the real-time geolocation of images in citrus croplands. To address the lack of public data, a comprehensive database was created and manually labelled at the pixel level to provide accurate training data for a deep learning approach. To reduce annotation effort, we developed a custom automation algorithm for pixel-wise labelling in complex natural backgrounds. A SegNet architecture with a Visual Geometry Group 16 (VGG16) backbone was trained for the semantic, pixel-wise segmentation of citrus foliage. The model was successfully integrated as a modular component within a broader system architecture and was tested with UAV-acquired images, demonstrating accurate disease detection and quantification, even under varied conditions. The developed system provides a robust tool for the efficient monitoring of citrus crops in precision agriculture. Full article

Background: Single-step genomic BLUP (ssGBLUP) has gained increasing interest from forest tree breeders. ssGBLUP combines phenotypic and pedigree data with marker data to enhance the prediction accuracy of estimated breeding values. However, potential errors in determining progeny relationships among open-pollinated species may result in lower accuracy of estimated breeding values. Unknown parent groups (UPG) and metafounders (MF) were developed to address missing pedigrees in a population. This study aimed to incorporate MF into ssGBLUP models to select the best parents for controlled mating and the best progenies for cloning in a tree breeding population of Eucalyptus globulus. Methods: Genetic groups were defined to include base individuals of similar genetic origin. Tree growth was measured as total height (TH) and diameter at breast height (DBH), while disease resistance was assessed through heteroblasty (the transition from juvenile to adult foliage: ADFO). All traits were evaluated at 14 and 21 months. Two genomic multi-trait threshold linear models were fitted, with and without MF. Also, two multi-trait threshold-linear models based on phenotypic and pedigree information (ABLUP) were used to evaluate the increase in accuracy when adding genomic information to the model. To test the quality of models by cross-validation, the linear regression method (LR) was used. Results: The LR statistics indicated that the ssGBLUP models without MF performed better, as the inclusion of MF increased the bias of predictions. The ssGBLUP accuracy for both validations ranged from 0.42 to 0.68. Conclusions: The best model to select parents for controlled matings and individuals for cloning is ssGBLUP without MF. Full article

The control of bacterial plant diseases is very challenging and often relies on the application of copper compounds, although the frequent emergence and spread of resistant bacterial strains compromise their efficacy. Additionally, copper-based compounds raise environmental and human health concerns, leading to their inclusion in the European Commission’s list of candidates for substitution. As a promising and sustainable alternative, we investigated the efficacy of biogenic zinc oxide nanoparticles (ZnO-NPs) in protecting tomato plants against Pseudomonas syringae pv. tomato (Pst), the causal agent of bacterial speck disease. ZnO-NPs exhibited significant in vitro antibacterial activity (EC₉₅ = 17.0 ± 1.1 ppm) against the pathogen. Furthermore, when applied to the foliage of tomato plants at 100 ppm before or following Pst inoculation, they induced significant reductions in symptom severity and bacterial growth in planta, which were comparable to those shown by plants treated with acibenzolar-S-methyl, a plant defense inducer. Gene expression assessed by qPCR revealed the involvement of the systemic acquired resistance (SAR) pathway in tomato plants treated with ZnO-NPs before inoculation, suggesting that the observed protection could be due to a priming effect. Finally, infected plants showed oxidative stress, with higher H₂O₂ and malondialdehyde (MDA) contents. ZnO-NPs reverted this effect, containing the content of the above molecules, and stimulated the production of metabolites involved in dealing with oxidative perturbations (carotenoids and phenols), while unaffecting flavonoids and anthocyanins. Full article

Hop latent viroid (HLVd), a 256-nucleotide RNA strand with complementary base-pairing and internal stem loop structures, forms circular or rod-shaped molecules within diseased plants. RT-PCR/RT-qPCR was used to assess HLVd transmission, spread and longevity. The viroid was detected in asymptomatic stock plants and in rooted vegetative cuttings, as well as in recirculated nutrient solution sampled from propagation tables and nozzles. Plant-to-plant spread through root infection in hydroponic cultivation was demonstrated. The viroid survived for 7 days and 4 weeks, respectively, in crushed leaf extracts (sap) or dried leaves/roots at room temperature. Following stem inoculation with infectious sap, HLVd was detected in root tissues within 2–3 weeks and in the foliage within 4–6 weeks. Plants grown under a 12:12 h photoperiod to induce inflorescence development showed more rapid spread of HLVd compared to 24 h lighting. The viroid was subsequently detected in inflorescence tissues, in trichome glands, in dried cannabis flowers and in crude resinous oil extracts. Anthers and pollen from infected male plants and seeds from infected female plants contained HLVd, giving rise to up to 100% infected seedlings. Artificially inoculated tomato and tobacco plants supported viroid replication in roots and leaves. Infected cannabis leaf and root tissues treated with UV-C for 3–5 min or temperatures of 70–90 °C for 30 min contained amplifiable HLVd-RNA. Infectious plant extract treated with 5–10% bleach (0.825% NaOCl) or 1000 ppm hypochlorous acid yielded no RT-PCR bands, suggesting the RNA was degraded. Meristem tip culture from HLVd-infected plants yielded a high frequency of pathogen-free plants, depending on the genotype. Full article

The genus Quercus (Fagaceae) is one of the most widely distributed and species-diverse trees in the Northern Hemisphere. The present study addresses the investigation of the phyto-chemical profile by ten assays, the antioxidant activity scavenging of DPPH radicals, total phenolic content, total flavonoids, and antimicrobial activity against three pathogenic bacteria with the foliage of two species of red oak (Quercus sartorii and Quercus rysophylla). Both species of oak showed a high phenolic content in the aqueous extract (22,342.10 ± 3076.5 mg GAE/kg of plant and 17,747.14 ± 1139.9 mg GAE/kg of plant, respectively). In the flavonoid content, Q. sartorii showed a higher amount in the ethanolic extract (24,587.42 ± 996.3 mg QE/kg of plant), while for Q. rysophylla, it was methanolic extract (19,875.66 ± 2754.01 QE/kg of plant). In the DPPH radical scavenging activity, Q. sartorii showed the highest percentage of inhibition in the methanolic extract (81.14 ± 1.7%), while in Q. rysophylla, it was the ethanolic extract (82.60 ± 2.7%). In the antimicrobial tests, inhibition halos were obtained in the strains Acinetobacter baumannii and Staphylococcus aureus of both species. All this gives a guideline to comprehensively elucidate the metabolites present in these two species for further study and application in the dispute against pathogenic bacteria or in diseases related to the imbalance of reactive oxygen species (ROS). Full article

In the realm of apple cultivation, the efficient and real-time monitoring of Gray Leaf Spot is the foundation of the effective management of pest control, reducing pesticide dependence and easing the burden on the environment. Additionally, it promotes the harmonious development of the agricultural economy and ecological balance. However, due to the dense foliage and diverse lesion characteristics, monitoring the disease faces unprecedented technical challenges. This paper proposes a detection model for Gray Leaf Spot on apple, which is based on an enhanced YOLOv8 network. The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. The experimental results show that the enhanced model outperforms the original YOLOv8 network in detecting Gray Leaf Spot. Notably, when the Intersection over Union (IoU) is 0.5, an improvement of 7.92% in average precision is observed. Therefore, this advanced detection technology holds pivotal significance in advancing the sustainable development of the apple industry and environment-friendly agriculture. Full article