Editor’s Choice Articles

Uncertainty remains over composting time and maturity degree for compost production. The objectives of this study were to establish maturity indicators for composting based on spectral and chemical components and to provide a reference for future composting management. Several indicators of composting were assessed for three commercial composts at 0, 7, 15, 30, 45, and 60 days during the germination of Chinese cabbage, including (1) central temperature, (2) moisture content, (3) pH, (4) electrical conductivity, (5) C/N ratio, (6) E4/E6 ratio, (7) fluorescence humification index (HIX), and (8) germination index (GI). We evaluated the optimal composting time using these indicators, reflecting the changes in hog manure, chicken manure, and agricultural by-product composts throughout their composting process to provide a basis for maturity time. The results showed that the E4/E6 ratio, C/N ratio, humic acid (HA), fulvic acid (FA), and germination rate, which reached a stable status after 30 days of composting, could be the indicators of “early-stage” maturity. In contrast, central temperature, electrical conductivity, HIX, and GI reached stable values after 45 days of composting and thus could be more suitable indicators of full maturity. Based on our results, we recommend a minimum composting time of 30 days to achieve primary maturity, while fully matured compost may be obtained after 45 days. Full article

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R²) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg⁻¹ for Cr, 20,963.84 ± 4447.83 mg kg⁻¹ for Fe, 1.46 ± 0.42 mg kg⁻¹ for Cd, and 48.71 ± 11.88 mg kg⁻¹ for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. Full article

Synergistically enhancing and realizing the value of grassland ecosystem services (ES) for economic activity is an important but challenging task for achieving sustainability in the karst desertification control (KDC). However, how to use grassland ES value characteristics in the KDC to make decisions on ES improvement, human well-being enhancement, and sustainable development remains unclear. In this paper, we took the contiguous region of karst desertification in Yunnan-Guangxi-Guizhou, China, a global hotspot, as the study area. Based on the valuation of the gross ecosystem product (GEP) and county economic intensity, we analyzed the structural and spatial characteristics of grassland GEP in the KDC using spatial analysis methods. We found that: (1) the grassland GEP in the KDC is mainly distributed in counties with low economic intensity (86.05% of the total number of counties) and vulnerable to losses caused by the livelihood of farmers; (2) the grassland GEP in the KDC is spatially small and scattered (the geographic concentration lies between 0.015 and 0.237), which makes it difficult to form industrial scale advantages; (3) the public product index (66.22–96.77%) and industry scale concentration (97.87–99.86%) of grassland GEP in the KDC are high, and most of the GEP is difficult to transform on the private market. Based on our findings, we proposed three corresponding recommendations for economic decision-making. The results of this study can provide a reference for economic decision-making regarding the management of grassland ES in karst areas with similar conditions and beyond. Full article

As United States farmers adapt soybean (Glycine max) production methods from oilseed to vegetable (edamame), key management practices will need to be considered. The key objective of this study was to determine the optimal nitrogen (N) rate and N application timing for edamame in the mid-Atlantic coastal plain system. The study was conducted for three years in Painter, VA, USA on sandy loam soils. A factorial arrangement of four N rates was applied with two application timing strategies: at-planting, and split application. Leaf tissue samples were collected and analyzed at R1. At harvest, the Normalized Difference Vegetation Index (NDVI) was measured, whole pods were mechanically collected, and yield was recorded. Additionally, pod and bean physical and chemical quality were assessed. Nitrogen fertilization significantly increased pod yield in two out of three years. R1 leaf N and sulfur (S) concentrations correlated to the yield, and R1 leaf and R6 whole-plant N concentrations correlated to the total N uptake. None of the tested parameters indicated that N fertilizer decreased yield or quality. In conclusion, we found that N fertilizer applied at planting may aid edamame yield and profit for sandy loam soils in the mid-Atlantic, USA. Full article

Satellite and sensor-based systems of site-specific fertilization have been developed almost exclusively in conventional farming. Agronomic and ecological advantages can also be expected from these digital methods in organic farming. However, it has not yet been investigated whether the algorithms and models are also applicable under organic farming conditions. In this study, the digital data and systems tested in the years 2021 and 2022 in southern Germany were (a) reflectance measurements with a tractor-mounted multispectral sensor, calculation of the vegetation index REIP, and application of algorithms; (b) satellite data in combination with the plant growth model PROMET; and (c) determination of the vegetation index NDVI based on satellite data. They were used to determine plant parameters (crop yield, biomass potential) and to calculate nitrogen balances at a high spatial resolution (10 × 10 m). The digital systems were tested at two sites with different organic farming systems (arable farming and dairy farming). Validation of the digital methods was carried out with ground-truth data from manual biomass sampling and combine harvester yield measurement. The nitrate leaching risk from the crop rotations of the farms was analyzed via site-specific N balancing using multi-year satellite data. The N balances were validated by measuring nitrate concentrations in leakage water. Additionally, soil properties, such as soil organic carbon (SOC) and total nitrogen (TN), were measured at the sub-field level. Using geostatistics, plant data, soil properties, and nitrate measurements were transferred into grids of the same resolution to enable correlation analyses. The correlations between yield determined with digital systems and the validation data were up to r = 0.77. Site-specific N balancing showed moderately positive correlations with nitrate concentrations in leakage water (r = 0.50–0.66). The strongly positive influence of the soil properties SOC and TN on crop yields underlines the importance of soil organic matter on soil fertility and site-specific yield potentials. The results show that digital methods allow the spatially high-resolution determination of yields and nitrogen balances in organic farming. This can be the basis for new management strategies in organic farming, e.g., the targeted use of limited nutrients to increase yields. Further validations under differentiated soil, climate, and management conditions are required to develop remote and proximal sensing applications in organic farming. Full article

Barley (Hordeum vulgare) grain stands out among other cereals due to its high nutritional value. It results mainly from the high content of fiber and antioxidants, such as phenolic compounds. Barley grains can also be an important source of unsaturated fatty acids and carotenoids that are beneficial to health. This study assessed the effect of the foliar application of an amino-acid biostimulant on the content of phenolic compounds, carotenoids, and the composition of fatty acids in the grain of alternative, black-grain barley genotypes, and the commonly used ‘Soldo’ cultivar, grown in conventional and organic farming systems. The dark-pigmented grains contained significantly more phenolic acids and flavonoids than the yellow seed of the traditional cultivar and were characterized by a significantly higher proportion of unsaturated fatty acids. The application of the biostimulant significantly increased the concentration of phenolic compounds in grains, especially of alternative genotypes. Full article

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment. Full article

Reasonable cropping systems and fertilizer management are vital for improving the quality of barren soil. The effectiveness of different crop rotation methods and fertilizers in soil improvement depends on various factors, including soil type, climate conditions, and crop type. In the present study, based on three rice-based cropping systems, the effects of organic fertilizers combined with slow-release fertilizers on rice yield and soil fertility in reclaimed soil were analyzed. The results showed that the rice grain yield was highest under the rice-fallow rotation system (RF) with the application of rapeseed meal fertilizer. Available nutrients such as AN, N_NH₄⁺, TP, and AK showed a significant positive correlation with rice grain yield (p < 0.05). PCA and PERMANOVA analysis supported significant variation in CAZyme abundance among cropping systems (R² = 0.60, p = 0.001) and significant differences between slow-release fertilizer treatments and organic fertilizer treatments (p < 0.05), but not among the three organic fertilizer treatments. Network analysis indicated positive stronger correlations among all functional enzymes in organic fertilizer treatments compared to chemical fertilizer treatments. RDA and correlation heat map results showed that C/N ratios and N_NH₄⁺ were strongly related to CAZyme composition. PLS-PM analysis revealed that soil available nitrogen positively influenced several variables, while rice grain yield was negatively influenced by soil enzymes and TOC. These findings suggested that under appropriate cropping systems, partially substituting chemical fertilizers with organic fertilizers can effectively enhance the availability of nutrients in the soil, alter the activity of carbon-cycling microorganisms, and increase rice grain yield. Full article

Purple soil is a type of global soil that is referred to by various names in different countries, which makes it difficult to understand, utilize, and ameliorate purple soil internationally. Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB) are the most widely used soil classification systems in the world. The aim of this study was to clarify the classification of purple soil in ST and the WRB and to establish a reference between different classification systems of purple soil. Therefore, based on the current principles and methods of the ST and WRB systems, 18 typical purple soil profiles in the eastern Sichuan Basin were identified, retrieved, and classified. Then, the soil units of the WRB were compared with those of ST and the Chinese Soil Taxonomy (CST). The results revealed that the 18 typical purple soil profiles could be classified into three soil orders, four soil group orders, and seven soil subgroups in ST and four reference soil groups (RSGs) in the WRB; each profile had its own unique principal and supplementary qualifier combinations within the soil units. It was found that when compared with the ST system, the WRB and CST systems had stronger abilities to distinguish purple soil. In addition, the WRB system was able to more comprehensively consider soil characteristics such as soil layer thickness, ferric horizon, soil color, texture mutations, and carbonate through qualifiers. However, the CST system added diagnostic characteristics, such as the lithologic characteristics of purplish sandstones and shales and the ferric properties and alic properties at the soil group and subgroup levels, which enhanced the differentiation ability of the purple soil at the subgroup level. Full article

As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment. Full article

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot. Full article

The European Green Deal has set a concrete strategic plan to increase farm sustainability. At the same time, the current global challenges, due to climate change and fuels and commodity market crises, combined with the COVID-19 pandemic and the ongoing war in Ukraine, affect the need for quality food and necessitate the reduction of negative external effects of agricultural production, with fair remuneration for the farmers. In response, precision agriculture has great potential to contribute to sustainable development. Precision agriculture is a farming management system that provides a holistic approach to managing the spatial and temporal crop and soil variability within a field to improve the farm’s performance and sustainability. However, farmers are still hesitant to adopt it. On these premises, the study aims to evaluate the impacts of precision agriculture technologies on farm economic, agronomic, and environmental management by farmers adopting (or not) these technologies, using the case study method. In detail, the work focuses on the period 2014–2022 for two farms that cultivate durum wheat in central Italy. The results suggest that the implementation of precision technologies can guarantee economic and agri-environmental efficiency. The results could serve as a basis for developing a program to start training in farms as well as to suggest policy strategies. Full article

Seed germination is a complex process involving several stages, starting with the imbibition of water and ending with the emergence of the radicle. In the current study, we address the observation of an unexpected pH shift during the imbibition of maize grains. We used direct pH measurements of soak water, the pH indicator methyl red, and anatomical analysis to shed light on the acidification associated with maize (Zea mays L.) germination, a largely overlooked phenomenon. Our work shows that acidification during imbibition of maize grains is a two-step process: (i) early, rapid acidification (pH values up to 4.4), in which protons stored in the (dead) pericarp/testa are mobilised and rapidly diffuse into the surrounding medium, and (ii) late, delayed acidification (pH values just below 6), starting hours after contact of grains with water, representing an active transport process caused by living cells of the seed. We discuss the physiological mechanisms and ecological relevance of environmental acidification during maize grain germination. Full article

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively. Full article

In order to investigate the crucial role of ZmBT1 in starch accumulation during maize grain development and analyze the expression and distribution of ZmBT1 in various maize tissues, we prepared a polyclonal antibody. Specifically, we successfully expressed the recombinant plasmid pGEX-6p-ZmBT1-C (382-437aa) and purified Gst-ZmBT1-C as the antigen for antibody preparation. Our results confirmed that the ZmBT1 protein in maize tissues can be specifically recognized by the ZmBT1 antibody. Through Western blotting, we observed that the expression protein of ZmBT1 varied by tissues, with the highest content in the grain and endosperm. Furthermore, we employed a combination of Western blotting and quantitative real-time PCR to show that the expression level of ZmBT1 can be influenced by plant hormones. This finding suggests that ZmBT1 plays a critical role in the accumulation of starch and opens up new avenues for functional studies of this protein. Full article

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate it with suitable regression algorithms for reliable prediction models are poorly understood. In this study, we analyzed 108 soil samples from Changting County, Fujian Province, China. Our objective was to evaluate the performance of various combinations of six feature selection methods and four regression algorithms for SOC prediction. Our findings are as follows: the combination of the Successive Projections Algorithm (SPA) and Partial Least Squares (PLS) yielded the most favorable results, with R² (0.61), RMSE (1.77 g/kg), and MAE (1.48 g/kg). Moreover, we determined the relative importance of variables, with the following ranking: 696 nm > 892 nm > 783 nm > 1641 nm > 1436 nm > 396 nm > 392 nm > 2239 nm > 2129 nm. Notably, 696 nm exhibited the highest importance in the SPA-PLS model, with the Variable Importance in Projection (VIP) value of 1.22. This study provides profound insights into feature selection methods and regression algorithms for SOC prediction, highlighting the superiority of SPA-PLS as the optimal combination. Full article

Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec^® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg⁻¹, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management. Full article

Soil microbial communities have an important role in plant establishment and health. Particularly, the role of the soil microbiome in agriculture is of current interest. The study of microbial communities associated with purslane could open questions about the rational exploitation of the microbiota for sustainable agricultural purposes. In this study, the composition of the fungal and bacterial communities and the bacterial metabolic functions, associated with the rhizospheres of two purslane genotypes (one commercially available and one collected from the wild in Spain) were evaluated. The results showed a clear effect of purslane genotype on fungal and bacterial community composition and functional profiles. The bacterial community of the commercial purslane rhizosphere was characterized by more numerous metabolic pathways, mainly pathways related to Terpenoids and Polyketides, Carbohydrate, Lipid, and Amino Acid metabolism. By contrast, the rhizosphere bacterial community of the Spanish (wild) genotype was characterized by the enrichment of functions related to cellular processes such as cell motility and transport. We hypothesize that these differences could be due to differential effects of root exudate composition on the microbial functional community composition. This finding points out the need to consider differences in the functional characteristics of plant genotypes when selecting the beneficial microorganisms to be used as biofertilizers aiming to maximize plant growth and resistance to environmental stressors. Full article

Asparagus harvesting presents unique challenges, due to the variability in spear growth, which makes large-scale automated harvesting difficult. This paper describes the development of an asparagus harvesting robot system. The system consists of a delta robot mounted on a mobile track-based platform. It employs a real-time asparagus detection algorithm and a sensory system to determine optimal harvesting points. Low-level control and high-level control are separated in the robot control. The performance of the system was evaluated in a laboratory field mock-up and in the open field, using asparagus spears of various shapes. The results demonstrate that the system detected and harvested 88% of the ready-to-harvest spears, with an average harvesting cycle cost of $3.44\mathrm{s}\pm 0.14\mathrm{s}$. In addition, outdoor testing in an open field demonstrated a 77% success rate in identifying and harvesting asparagus spears. Full article

Changing the harvesting time of apples from the optimum harvest window to delayed harvesting may be applied if it is impossible to harvest apples at the optimal time, but it requires changing other factors, as they influence the quality of fruits and shelf life. The aim of the study was to assess the quality of ‘Red Jonaprince’ apples during storage after delayed harvesting and 1-methylcyclopropene (1-MCP) preharvest and postharvest treatment for various storage times. Apples were studied within four groups subjected to preharvest and postharvest treatments, as follows: Group 0—no 1-MCP treatment; Group 1—1-MCP preharvest treatment; Group 2—1-MCP postharvest treatment; and Group 3—1-MCP preharvest and postharvest treatment. All apples were subjected to ultra-low oxygen (ULO) storage conducted for 3, 5 or 6 months, while the analyses were conducted directly after ULO storage (simulated shelf life—0 days) and after simulated shelf life (7 days). For firmness, in the case of 1-MCP applied only preharvest (Group 1) and only postharvest (Group 2), before shelf life, the longer ULO storage resulted in obtaining lower values of firmness (p < 0.0001). If 1-MCP was not applied postharvest (Group 0 and Group 1), and short ULO storage was applied (3 and 5 months for Group 0; 3 months for Group 1), after shelf-life lower values of firmness were observed (p < 0.0001). For soluble solids content (SSC), in the case of 1-MCP not applied preharvest (Group 0 and Group 2), before shelf life, and for 1-MCP applied postharvest (Group 2) after shelf life, the longer ULO storage resulted in obtaining lower values of SCC (p < 0.0001). For titratable acidity (TA), in the case of all the studied groups after shelf life, as well as in case of 1-MCP applied only preharvest (Group 1) also before shelf life, the longer ULO storage resulted in obtaining lower values of TA (p < 0.0001). Except for the 1-MCP applied only postharvest (Group 2), in the case of short ULO storage applied (3 and 5 months for Group 0; 5 months for Group 1; 5 months for Group 3), after shelf-life lower values of TA were observed (p < 0.0001). If delayed harvesting must be conducted, applying 1-MCP not only postharvest, but also preharvest, allows obtaining the most stable firmness and SSC, which do not decrease during storage and shelf life. Taking this into account, it may be concluded, that in the case of delayed harvesting, combining 1-MCP applied preharvest and postharvest should be recommended to keep the quality parameters stable during storage and shelf life. Full article

Bamboo is nutritionally significant across the world because the shoots are high in calories and nutritional fiber but low in cholesterol. However, recent research has shown that bamboo shoots also contain a substantial quantity of heavy metals, including arsenic (As). Therefore, we explored whether the co-application of iron oxide nanoparticles (IONPs) and selenium nanoparticles (Se-NPs) would attenuate As toxicity in bamboo plants (Pleioblastus pygmaeus). A greenhouse experiment was performed to investigate plant responses to arsenic toxicity. Bamboo plants exposed to four levels of As (0, 10, 20, and 40 mg L⁻¹) were foliar-sprayed with 60 mg L⁻¹ of Se-NPs and 60 mg L⁻¹ of IONPs alone and in combination. The data indicated that different As concentrations (10, 20, and 40 mg L⁻¹) caused membrane damage and reactive oxide species (ROS) production in bamboo cells, characterized by H₂O₂, O₂^•−, MDA, and EL increasing by up to 47%, 54%, 57%, and 65%, respectively, in comparison with a control. The co-application of 60 mg L⁻¹ of Se-NPs + IONP markedly improved the antioxidant enzyme activities (by 75% in SOD, 27% in POD, 52% in CAT, 37% in GR, and 38% in PAL), total flavonoid content (42%), phenolic content (36%), proline (44%), nitric oxide (59%), putrescine (Put) (85%), spermidine (Spd) (53%), relative water content (RWC) (36%), photosynthetic characteristics (27%) in net photosynthesis (Pn) (24% in the intercellular CO₂ concentration (Ci), 39% in stomatal conductance (Gs), and 31% in chlorophyll pigments), and ultimately biomass indices and growth. The co-application of Se-NPs + IONPs with 10 and 20 mg L⁻¹ of As raised the TI by 14% and 9% in the shoot and by 18% and 14% in the root, respectively. IONPs and Se-NPs reduced ROS, cell membrane lipoperoxidation, and electrolyte leakage, all contributing to the decrease in oxidative stress by limiting As uptake and translocation. In sum, Se-NPs and IONPs improved bamboo endurance, yet the most effective approach for increasing bamboo’s ability to recover from As toxicity was the concurrent use of 60 mg L⁻¹ of Se-NPs and 60 mg L⁻¹ of IONPs. Our IONP and Se-NP data from single and combined applications offer novel knowledge in improving the tolerance mechanism against As exposure in Pleioblastus pygmaeus. Full article

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types. Full article

Melatonin (MT) is a growth regulator and antioxidant that can resist peroxidation damage on plants caused by environmental stresses. In this study, the alleviation effects of melatonin on alfalfa under salt stress were investigated in terms of photosynthesis, antioxidant enzymes, and osmoregulation. The alfalfa seedlings were cultured in 200 mM NaCl Hoagland solution. Five levels of MT (0, 0.1, 0.2, 0.3, and 0.4 mM) were applied as a foliar spray. Generally, the foliar spray of MT increased root length, root surface area, height, leaf length and width, aerial and root biomass, SPAD readings, the content of proline and soluble protein, and the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Malonaldehyde (MDA) content was decreased by MT foliar spray. The beneficial effects of MT on alfalfa under salt stress were dosage-dependent, and excessive MT levels inhibited alfalfa growth. The alleviating effects of MT on salt stress were more pronounced at 0.3 mM MT. This study suggested that exogenous MT foliar spray at appropriate levels can ameliorate the adverse effects of salt stress on alfalfa seedlings. Full article

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs. Full article

Functionality based on the biological activity of sorghum such as antioxidant activity is known worldwide for its excellence. In this study, we investigated the reactive oxygen species (ROS) scavenging activity, total phenolic and flavonoid contents, phenol compounds, and changes in antioxidant gene expression in sorghum seed cells collected from five countries (Australia, former Soviet Union, USA, Sudan, and Guadeloupe). Sorghum seeds were obtained from 12 genetic resources (K159041, K159042, K159078, K159081, K159088, K159089, K159093, K159097, K159100, K159096, K159048, and K159077). ROS scavenging activity was analyzed using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,20-azinobis 3-ethylbenzothiazoline-6-sulfonate (ABTS). K159097 showed high antioxidant activity values of 33.52 ± 0.70 μg/mL (DPPH) and 271.06 ± 13.41 μg/mL (ABTS), respectively. The reducing power of the resources improved in a concentration-dependent manner, and 10 sorghum resources, except K159078 and K159048, showed high reducing power. K159042 had the highest total phenol content (231 ± 2.17 mg·GAE/g), and K159081 had the highest total flavonoid content (67.71 ± 5.38 mg·QE/g). Among the six phenolic compounds (protocatechuic acid, caffeic acid, p-coumaric acid, ferulic acid, taxifolin, and naringenin) analyzed, the compound with the highest content was taxifolin (203.67 ± 4.99 mg/L in K159093). K159041, K159042, and K159048 had the highest expression levels of superoxide dismutase (SOD), ascorbate peroxidase 1 (APX1), and catalase (CAT), which are indicators of antioxidant activity. An evaluation of the diversity of sorghum provided useful information on antioxidant activity, physicochemical content, and antioxidant gene expression in seed cells, suggesting that sorghum can be used as a biomaterial from natural resources. Full article

The medicinal and aromatic plant (MAP) sector in Italy is a niche sector that is growing in terms of both primary production and consumption. These products seem to be important to address several global challenges, including climate change, biodiversity conservation, drought solutions, product diversification, product innovations, and the development of rural areas (rural tourism in primis). This study utilised the Delphi method to identify key factors and possible strategies that could be adopted for the future (the next 3–5 years) of the national MAP supply chain. The research involved the collaboration of 26 experts. Individual interviews, based on a semi-structured questionnaire, were carried out during the first round of the study. The information and the collected data were then analysed and depicted in a mental map. The Italian MAP sector suffers from competition from lower-cost imported products. Despite this, the experts predicted an expansion of the MAP sector regarding aromatic herbs and certain derivative products, such as dietary supplements, biocides, and essential oils. The experts anticipated the need to increase the adoption of digital innovations, of developing agreements among the actors of the supply chain, and of investing in the training of supply chain actors. Full article

White radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathematical model working on high-resolution images to measure radish’s biophysical properties automatically. A color calibration was performed on the dataset using a color checker panel to minimize the impact of varying light conditions on the RGB images. Subsequently, a Mask-RCNN model was trained to effectively segment different components of the radishes. The observations of the segmented results included leaf length, leaf width, root width, root length, leaf length to width, root length to width, root shoulder color, and root peel color. The automated real-life measurements of these observations were then conducted and compared with actual results. The validation results, based on a set of white radish samples, demonstrated the models’ effectiveness in utilizing images for quantifying phenotypic traits. The average accuracy of the automated method was confirmed to be 96.2% when compared to the manual method. Full article

Climate change has become the biggest global challenge, being a real danger especially for crops and an inevitable threat to food security. This paper presents the results of a study conducted in the Transylvanian Plain during 2012–2021, regarding the influence of climatic factors, such as temperature, rainfall, water reserve in the soil and hours of sunshine, on the development stages and yield of maize. During 2012–2021, the soil water reserve determined for maize cultivation was above the minimum requirements (1734.8 m³ ha⁻¹) in the spring months, but fell below this limit in the months when the water consumption for maize was the highest, but without reaching the withering index (1202.8 m³ ha⁻¹). The hours of sunshine in the maize vegetation period have been significantly reduced from 1655.5 h (2012) to values between 1174.6 and 1296.7 h, with a significant decrease in this parameter being observed. The coefficient of determination (R₂ = 0.51) shows the importance of rainfall during the period of emergence of reproductive organs in maize production. During 2019–2021, there was a decreasing trend of temperatures in May compared to the multiannual average of this month, and therefore the processes of emergence and growth of plants in the early stages were affected. During the period of the study, all parameters analyzed (temperature, rainfall, water reserve in the soil, hours of sunshine) deviated from the multiannual average, with negative variations compared to the requirements of maize. Climatic conditions, especially during the growing season, have a significant influence on the yield of a crop, especially when the interaction between several parameters is manifested. Full article

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination ($R^2$) and root mean squared error ($RMSE$). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection. Full article

The study, conducted to evaluate the effects of long-term straw management combined with the application of increasing nitrogen rates on the yield of twenty winter wheat varieties, as well as on soil properties, was carried out in a long-term field trial established in 1971. The trial was monitored for twenty growing seasons under rainfed conditions in a typical chernozem zone of the southern part of the Pannonian Plain. The cropping system was a winter wheat-maize-soybean rotation. The ten SN-treatments (combinations of straw management (S) and N-fertilization) were as follows: In the plot (treatment) with straw return (S₁), seven variants of nitrogen fertilization (0–180 kg N ha⁻¹) were included, while on the plot without straw return (S₀) the variants of N-fertilization were 0, 90 and 150 kg N ha^−l. Based on the high relative share in the total sum of squares, variance analysis showed that wheat grain yield (GY) was significantly affected by years, SN-treatments, and their interaction, and they can explain the largest part of the total variance of GY. The results showed that straw return integrated with N fertilization could increase wheat yield to varying degrees over 20 years. On average, for all years, the highest GYs were obtained in the treatment S₁ and fertilization with 180 and 150 kg N ha⁻¹. The overall results showed that long-term straw returning significantly increased GY by an average of 8.4 ± 4.5%, with a considerable simultaneous increase in yield stability compared to straw removal. In addition, straw incorporation (SI) significantly increased soil humus, total nitrogen (TN), and soil organic carbon (SOC) contents at a soil depth of 0–30 cm by an average of 4.2, 3.8, and 11.3%, respectively. The results of our study have demonstrated that the long-term practice of straw return, in combination with the application of mineral fertilizers, has the potential to serve as a sustainable soil management strategy that is economically viable and environmentally acceptable. However, additional research is required to investigate its interactive effects on both grain yield and soil productivity. Full article

The Hilbert–Huang transform (HHT) has been used as a powerful tool for analyzing nonlinear and nonstationary time series. Soil loss is controlled by complicated physical processes and thus fluctuates with nonlinearity and nonstationarity over time. In order to further clarify the relationship between rainfall, surface runoff, and sediment yield, this study adopted the HHT to analyze these characteristics through multiple time scales and investigated their relationship through time-dependent intrinsic correlation (TDIC) in the time series. A six-year study (2015–2020) was conducted on sloping farmlands to explore the relationships between soil loss and rainfall in southwest China. Time series of soil loss and rainfall were identified as the relevant characteristics at different time scales based on the method of HHT. Local correlation between the soil loss and runoff was carried out by the method of TDIC. The original time series of the rainfall, runoff, and soil loss were decomposed into eight intrinsic mode functions (IMFs) and a residue by ensemble empirical mode decomposition (EEMD). The residue indicated that the rainfall and runoff increased and then decreased during the maize-growing season from 2015 to 2020, whereas the soil loss gradually decreased. IMF1 and IMF2 accounted for nearly 80% of the temporal variations in rainfall, runoff, and soil loss, indicating that the variables varied the most at short time scales. The TDIC analysis showed that strong and positive correlations between the soil loss, rainfall, and runoff prevailed over the entire time domain at the scales of IMF1 and IMF2, indicating the rapid response of the soil loss to rainfall and runoff at short time scales. Time-varying correlations were observed at the IMF3–IMF5 scales. At the IMF7 scale, an evident switchover in the nature of the correlation was identified during the years 2018 and 2019; this could be related to a sudden rainstorm under low vegetation coverage conditions. The EEMD-based TDIC tool is an effective means to clarify the relationship between soil loss, rainfall, and runoff. Our results provide a better understanding of the relationship between soil loss and rainfall varied with time at multiple time scales. Short-term heavy rainfall and rapid surface runoff are the important factors causing serious soil and water loss on a short time scale in a mountainous region with yellow soil, which is of great significance for the construction of a regional soil erosion prediction model. Full article

Since impurities produced during walnut processing can cause serious harm to human health, strict quality control must be carried out during production. However, most detection equipment still uses photoelectric detection technology to automatically sort heterochromatic particles, which is unsuitable for detecting endogenous foreign bodies with similar colors. Therefore, this paper proposes an improved YOLOv4 deep learning object detection algorithm, WT-YOLOM, for detecting endogenous impurities in walnuts—namely, oily kernels, black spot kernels, withered kernels, and ground nutshells. In the backbone of the model, a lightweight MobileNet module was used as the encoder for the extraction of features. The spatial pyramid pooling (SPP) structure was improved to spatial pyramid pooling—fast (SPPF), and the model size was further reduced. Loss function was replaced in this model with a more comprehensive SIoU loss. In addition, efficient channel attention (ECA) mechanisms were applied after the backbone feature map to improve the model’s recognition accuracy. This paper compares the recognition speed and accuracy of the WT-YOLOM algorithm with the Faster R-CNN, EfficientDet, CenterNet, and YOLOv4 algorithms. The results showed that the average precision of this model for different kinds of endogenous impurities in walnuts reached 94.4%. Compared with the original model, the size was reduced by 88.6%, and the recognition speed reached 60.1 FPS, which was an increase of 29.0%. The metrics of the WT-YOLOM model were significantly better than those of comparative models and can significantly improve the detection efficiency of endogenous foreign bodies in walnuts. Full article

An apple-picking robot is now the most widely accepted method in the substitution of low-efficiency and high-cost labor-intensive apple harvesting. Although most current research on apple-picking robots works well in the laboratory, most of them are unworkable in an orchard environment due to unsatisfied apple positioning performance. In general, an accurate, fast, and widely used apple positioning method for an apple-picking robot remains lacking. Some positioning methods with detection-based deep learning reached an acceptable performance in some orchards. However, apples occluded by apples, leaves, and branches are ignored in these methods with detection-based deep learning. Therefore, an apple binocular positioning method based on a Mask Region Convolutional Neural Network (Mask R-CNN, an instance segmentation network) was developed to achieve better apple positioning. A binocular camera (Bumblebee XB3) was adapted to capture binocular images of apples. After that, a Mask R-CNN was applied to implement instance segmentation of apple binocular images. Then, template matching with a parallel polar line constraint was applied for the stereo matching of apples. Finally, four feature point pairs of apples from binocular images were selected to calculate disparity and depth. The trained Mask R-CNN reached a detection and segmentation intersection over union (IoU) of 80.11% and 84.39%, respectively. The coefficient of variation (CoV) and positioning accuracy (PA) of binocular positioning were 5.28 mm and 99.49%, respectively. The research developed a new method to fulfill binocular positioning with a segmentation-based neural network. Full article

It is of great importance to study the changes in reference evapotranspiration (ET₀) and the factors that influence it to ensure sustainable and efficient water resource utilization. Daily ET₀ data calculated using the Penman–Monteith method from 37 meteorological stations located within Guangdong Province in the humid zone of southern China from 1960 to 2020 were analyzed. The trend analysis and Mann–Kendall test were used to analyze the time series changes in ET₀ and major climatic factors (air temperature (T), relative humidity (RH), sunshine duration (SD), and wind speed (u₂)) for over 61 years. Sensitivity and contribution analyses were used to evaluate the driving factors of ET₀. The main findings of the study are as follows: (1) the trend in average annual ET₀ time series in Guangdong slightly increased at a trend rate of 1.61 mm/10a over the past 61 years, with most stations experiencing an increase in ET₀. During the same period, air temperature significantly increased, while RH and SD decreased; u₂ also decreased. (2) Sensitivity analysis showed that ET₀ was more sensitive to RH and T than SD and u₂, with ET₀ being most sensitive to RH in spring and winter and T in summer and autumn. (3) The contribution analysis showed that T was the dominant factor for ET₀ variation in Guangdong, followed by SD. SD was found to be the dominant factor in ET₀ changes in areas where the “evaporation paradox” occurred, as well as in spring and summer. The study concludes that the climate in Guangdong became warmer and drier over the past 61 years, and if the current global warming trend continues, it will lead to higher evapotranspiration and drought occurrence in the future. Full article

China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high and causes waste of fertilizer resources and ecological environmental damage. To address the above problems, a hybrid optimization of genetic algorithms and particle swarm optimization (GA–PSO) is used to optimize the initial weights of the backpropagation (BP) neural network, and a hybrid optimization-based BP neural network PID controller is designed to realize the accurate control of fertilizer flow in the integrated water and fertilizer precision fertilization control system for field crops. At the same time, the STM32 microcontroller-based precision fertilizer application control system for integrated water and fertilizer application of large field crops was developed and the performance of the controller was verified experimentally. The results show that the controller has an average maximum overshoot of 5.1% and an average adjustment time of 68.99 s, which is better than the PID and PID control algorithms based on BP neural network (BP–PID) controllers; among them, the hybrid optimization of PID control algorithm based on BP neural network by particle swarm optimization and genetic algorithm(GA–PSO–BP–PID) controller has the best-integrated control performance when the fertilizer application flow rate is 0.6m³/h. Full article

Optimizing profitability is a challenge that strawberry farmers must face in order to remain competitive. Within this framework, plant density can play a central role. The aim of this two-year study was to investigate how planting density can induce variations in plant growth and yield performances in an alpine mountain strawberry cultivation (Martell Valley, South Tyrol, Italy), and consequently quantify the farm profit. Frigo strawberry plants cv. Elsanta were planted in soil on raised beds and subjected to five different planting density levels (30,000 and 45,000 as large spacing; 60,000 as middle spacing; 90,000 and 100,000 plants ha⁻¹ as narrow spacing, corresponding to a plant spacing of 28, 19, 14, 9, and 8.5 cm, respectively). Our findings indicate that the aboveground biomass in plants subjected to low planting density was significantly increased by +50% (end of first year) and even doubled in the second year in comparison with plants in high planting density. Those results were related to higher leaf photosynthetic rate (+12%), and the number of crowns and flower trusses per plant (+40% both) (p < 0.05). The low yield (about 300 g plant⁻¹) observed in the high planting density regime was attributable to smaller fruit size during the first cropping year and to both a reduced number of flowers per plant and fruit size during the second year (p < 0.05). Although the highest yield (more than 400 g plant⁻¹) was obtained with wide plant spacing, the greatest yield per hectare was achieved with high planting densities (28 t ha⁻¹ in comparison with 17 t ha⁻¹ with low plant density level). However, the farm profit must take into account the costs (especially related to the plant material and harvesting costs) that are higher under the high planting density compared with the other density regimes. Indeed, the maximum farm profit was reached with a density of 45,000 plants ha⁻¹ which corresponded to EUR 22,579 ha⁻¹ (over 2 years). Regarding fruit quality, fruits coming from the low plant density level showed a significantly higher color index (+15% more red color) than fruits from high plant density (p < 0.05). In conclusion, our results suggest that a middle planting density can be a fair compromise in terms of plant growth, yield, and farm profit. Full article

Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation. Full article

Organic biostimulators support wheat growth in unfavorable conditions; however, to date, multifactorial assessments of their role in the plant–pesticide–pathogen system have been poorly investigated. The goal of this study was to evaluate the changes in the metabolite profile (protein, carbohydrate, phenolic compounds, acid phosphatases, and amino acids) and the antioxidant potential (antioxidant enzymes) of wheat that is infested with F. culmorum and exposed to humic acids, nitrophenols, and six pesticides. Additionally, the concentration of the mycotoxins in the wheat grain and the dissipation time of the six pesticides in the wheat plants were determined. In this multifactorial experiment, we explored differentiated activities of humic acids and nitrophenols in wheat metabolism during fungal pathogenesis and pesticide protection. Nitrophenols decreased oxidative stress through induced catalase activity. In contrast, humic acids contributed to the highest enhancement of the total level of carbohydrates (27%) in the inoculated wheat. Both biostimulators reduced the mycotoxin concentration (DON, 3-AcDON, 15-AcDON, NIV) by 32% and nitrophenols increased the concentration of amino acids (13%). Unexpectedly, humic acids and nitrophenols shortened the degradation time (DT₅₀) of spiroxamine by up to 60% in inoculated wheat. The overall results of this study provide novel information on the changes in wheat metabolites, antioxidant defense, and pesticide dissipation in the pesticide–biostimulator–pathogen system. Full article

Rice culture is one of the most important crops in the world, being the most consumed cereal grain (755 million tons in 2020). Since rice is usually produced under flooding conditions and water performs several essential functions for the crop, estimating its water needs is essential. Remote sensing techniques have shown effectiveness in estimating and monitoring the water use in crop fields. An estimation from satellite data is a challenge, but could be very useful, in order to spatialize local estimates and operationalize production models. This study intended to derive an approach to estimate the actual crop evapotranspiration (ET_a) in rice paddies from a temporal series of satellite images. The experimental data were obtained in the Lis Valley Irrigation District (central coast of Portugal), during the 2019 to 2021 rice growing seasons. The average seasonal ET_a (FAO56) resulted 586 ± 23 mm and the water productivity (WP) was 0.47 ± 0.03 kg m⁻³. Good correlations were found between the crop coefficients (K_c) proposed by FAO and the NDVI evolution in the control rice fields, with R² ranging between 0.71 and 0.82 for stages II+III (development + middle) and between 0.76 and 0.82 for stage IV (late). The results from the derived RS-assisted method were compared to the ET_a values obtained from the surface energy balance model METRIC, showing an average estimation error of ±0.8 mm d⁻¹, with a negligible bias. The findings in this work are promising and show the potential of the RS-assisted method for monitoring ET_a and water productivity, capturing the local and seasonal variability in rice growing, and then predicting the rice yield, being a useful and free tool available to farmers. Full article

Legumes’ nutrition relies on two sources of nitrogen (N): mineral N from soil, and biological N fixation (BNF). The aim of this study was to verify the effect of bacterial inoculation, as well as to compare it with the effect of different mineral N fertilization on the main nodulation characteristics, yield components and seed yield of two soybean (Glycine max (L.) Merr.) cultivars in the conditions of south-eastern Poland. A randomized block design was used with four replications and combining the application rates of mineral N (0, 30 and 60 kg·ha⁻¹), and seed inoculation with Bradyrhizobium japonicum (HiStick^® Soy and Nitragina) were applied for two soybean cultivars (Aldana, Annushka). It has been shown that inoculation of B. japonicum increases the nodulation on plant roots, yield components and seed yield, but no significant effect of the bacterial preparation used on the seed yield was observed. The application of 30 kg N·ha⁻¹ did not result in a significant reduction in the number and weight of nodules, including on the main root and lateral roots, compared to seeds inoculated and not fertilized with N, as observed under a dose of 60 kg N·ha⁻¹, but resulted in an increase in the number of pods and the number and weight of seeds per plant. For both soybean cultivars, the best combination was nitrogen fertilization at 30 kg N·ha⁻¹ and seed inoculation with B. japonicum, regardless of the bacterial preparation used. Full article

Liquid fertilizers are often used in the middle of the growing season in an attempt to enhance organic carrot (Daucus carota var. sativus) yield and quality, although their effect on plant performance is unproven. The impact of liquid organic fertilizers and foliar seaweed applications on carrot yield and quality characteristics were evaluated on certified organic land at the Colorado State University Horticulture Field Research Center in Fort Collins, CO, USA, in 2014 and 2015. Hydrolyzed and non-hydrolyzed fish fertilizer and cyanobacterial fertilizer (cyano-fertilizer) treatments were applied through a drip irrigation system at prescribed N rates about every 10 days throughout the growing season. Each treatment, including the unfertilized control, was repeated with the addition of concentrated organic seaweed extract, containing phytohormones, applied foliarly at the manufacturer’s recommended rates. The cyano-fertilizer treatment resulted in longer carrots in 2014 and the highest carrot yield in both years, with it consistently yielding equal to or greater than either hydrolyzed or non-hydrolyzed fish fertilizer. The foliar seaweed applications had no effect on carrot yield in either year. The cyano-fertilizer performed comparably to the other fertilizers, suggesting that cyano-fertilizer could be a viable alternative to organic liquid fish fertilizers. Full article

During the early growth stage of plants, low temperatures can alter cell permeability, reduce photosynthetic capacity, and have adverse effects on crop growth, development, and yield. Different strawberry cultivars have varying cold tolerance. In this study, we investigated the changes in cell permeability and photosynthetic activity of short-day and long-day types of strawberry cultivars under varying degrees of low-temperature stress, and evaluated the extent of cellular damage using photosynthetic and chlorophyll fluorescence parameters. The experiment utilized short-day strawberry cultivars ‘Toyonoka’ and ‘Red Face’, and long-day strawberry cultivars ‘Selva’ and ‘Sweet Charlie’ seedlings. Low-temperature treatments were set at −20, −15, −10, −5, 0, 5, and 10 °C for 12 h. The research demonstrated that short-day strawberries had greater tolerance to low temperatures, and all four strawberry cultivars began to experience low-temperature stress when the temperature was below 5 °C. A temperature range of 0 to −10 °C played a crucial role in causing severe cold damage to the strawberries. The low-temperature stress levels were constructed based on electrolyte leakage, with photosynthetic physiological characteristics serving as references. The study proves that the photosynthetic and chlorophyll fluorescence parameters can serve as effective probes for diagnosing low-temperature stress in strawberry seedlings, and their combination provides higher accuracy in identifying stress levels than any single type of parameter. Full article

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control. Full article

Digestate contains many essential nutrients for crops, including nitrogen (N) and phosphorus (P), and it can alter the biogeochemical cycle of nutrients and soil functionality. This work aimed to assess the fertilizing effects of digestate on chemical and biological soil properties in a field experiment in eastern Portugal with two horticultural crops involving nine treatments: control without fertilization; mineral N fertilization with 85 kg ha⁻¹; fertilization with digestate (DG) with increasing N rates (85, 170, 255, or 340 kg N ha⁻¹); and fertilization with different combinations of digestate plus mineral N (DG at 85 or 170 kg N plus 60 kg mineral N ha^–1 or DG at 170 kg N plus 25 kg mineral N ha^–1). In addition to N, digestate supplied significant amounts of P, Ca, K, and Mg and significantly increased soil Olsen P, mineral N, and organic C. At high doses, it decreased phosphatase and β-glucosidase activities, as well as fungi and bacterial biomass, compared to the control or mineral N fertilization, and it also negatively affected soil P and C cycling capacity and microbial biomass. The organic to total N ratio and the N to P ratio in digestate are crucial properties for evaluating its agronomic management as fertilizer. Full article

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ET_c). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m² m⁻² to 11.28 m² m⁻² and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R² = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p < 0.05) but not practical differences (<15%) between pre-defined zones. UAV imagery coupled with field average canopy height can be a useful tool for predicting LAI in alfalfa. Full article

Presented research was carried out in 2021 and 2022 on the Felix soybean variety at the Agricultural Research and Development Station Turda, located in the Transylvanian Plain, Romania. In this experiment, complex fertilizer NPK 20:20:0 was applied as a basic fertilizer in a dose of 200 kg ha⁻¹ at the sowing stage, to which foliar fertilizer Agro Argentum Forte treatment was added in different doses and at different application stages. The main purpose of the study was to identify the suitable stages of foliar application in soybean cultivation for effective vegetative development, yield, and quality purposes. The impacts of the fertilization system and the climatic conditions on the physiological parameters, assimilation, yield, and quality were evaluated. Technology showed that the physiological parameters were positively influenced, following the foliar fertilization with Agro Argentum Forte, with average assimilation values recorded above 23.0 μmol CO₂ m⁻²s⁻¹ in the year 2021 and 22.4 μmol CO₂ m⁻²s⁻¹ in the year 2022. Soybean crop was influenced by climatic conditions and the application of foliar fertilizers in different phases of growth and development, obtaining higher yields, as well as higher protein and oil content. The soybean yield and quality indices (protein, oil, and mass of a thousand seeds) were higher in 2021 than in 2022 for the variants treated with foliar fertilizers compared to the control, resulting in an improvement in seed quality in 2021 with a yield of 3560 kg ha⁻¹, while 2022 saw a lower yield of 1805 kg ha⁻¹. The application of basic mineral fertilizers in combination with foliar fertilization had a significantly positive impact on the quality indicators of soybean seeds. The highest yields were achieved when the foliar treatment was applied in the early pod formation stage. Full article

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R²) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R² = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R² = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R² = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R² and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data. Full article

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management. Full article

Background: There is increasing evidence that the reliance on synthetic chemical pesticides and mineral fertilizers in agriculture has significant negative environmental and/or health impacts and poses a risk for future food security. Systematic reviews/meta-analyses showed that organic production systems, which omit the use of agrochemicals, produce crops with lower yields, but superior nutritional composition. However, the agronomic parameters responsible for differences in crop yields and nutritional quality are poorly understood. Methods: Here we report results for four field vegetable crops from the Nafferton Factorial Systems Comparison (NFSC) trial. This long-term factorial field experiment was designed to (i) identify effects of growing season/climatic variation, and contrasting rotational designs, crop protection protocols and fertilization regimes used in organic and conventional systems on crop health, yield and nutritional parameters and (ii) estimate the relative importance of climatic and agronomic drivers for crop health, yield and nutritionally relevant quality parameters. Quality parameters monitored in harvested products, included phenolic, glucosinolate, vitamin C, vitamin B₉, carotenoid, cadmium (Cd), nickel (Ni), lead (Pb) and glycoalkaloid concentrations. Results: Climatic conditions during the growing season were found to have a larger impact on crop yield and quality than the agronomic factors (pre-crop, crop protection, fertilization) studied. However, the (i) interactions between growing season with contrasting climatic conditions and agronomic factors identified by ANOVA for crop health, yield and quality parameters and (ii) the associations between the three climatic drivers (precipitation, temperature, radiation) and crop yield and quality parameters differed substantially between the four crop plant species. Among the agronomic factors, fertilization had a substantially larger impact compared with both pre-crop and crop protection. Specifically, crop yields were found to be significantly increased by the use of (i) conventional fertilization and crop protection methods in potato, (ii) conventional fertilization, but organic crop protection methods in cabbage, and (iii) conventional fertilization regimes in lettuce, while none of the agronomic factors had a significant effect on onion yields. When important crop pest and diseases were assessed, (i) conventional crop protection resulted in significantly lower late blight severity in potato, while (ii) organic crop protection resulted in lower bird damage and cabbage root fly (CRF) incidence in cabbages, and Sclerotinia incidence in lettuce and (iii) organic fertilization resulted in lower CRF and Sclerotinia incidence in cabbage and lettuce respectively. When concentrations of nutritionally relevant phytochemicals were compared, organic fertilization resulted in significantly higher phenolic concentrations in potato, cabbage and lettuce, higher glucosinolate and carotenoid concentrations in cabbage, higher vitamin C concentrations in potato and cabbage and higher vitamin B₉ concentrations in potato and lettuce—but lower concentrations of toxic glycoalkaloids in potato. Significant effects of crop protection protocols on phytochemical concentrations were only detected in cabbage with conventional crop protection resulting in higher glucosinolate and vitamin B₉ concentrations. When toxic metal concentrations were compared, organic fertilization resulted in significantly lower Cd concentrations in all four crops and lower Ni concentrations in potato, cabbage and onion. Significant effects of crop protection were only detected in cabbage, where organic crop protection resulted in lower Ni concentrations. Pb concentrations were not affected by any of the agronomic factors. The potential implications of results for improving (i) strategies to reduce the use of non-renewable resources and environmental impacts of vegetable production and (ii) the productivity of organic and other low-input vegetable production systems without compromising food quality are discussed. Conclusions: The study confirms that organic vegetable production protocols result in higher concentrations of phenolics and other nutritionally desirable phytochemicals, but lower concentrations of the toxic metals Cd and Ni in harvested products. It also demonstrates, for the first time, that this is primarily due to differences in fertilization regimes. The finding that in three of the four crops (cabbage, lettuce and onion) the application of synthetic chemical crop protection products had no measurable positive impact on crop health and yield should be considered in the context of the growing concern about health impacts of pesticide use in field vegetable crops. Full article

We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits. Full article