Search Results (3,264)

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma,ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts. Full article

One of the main challenges in modern animal husbandry in North Kazakhstan is ensuring an uninterrupted supply of sufficient fodder crops. This research, conducted from 2019 to 2023, aimed to develop strategies for cultivating environmentally sustainable fodder crops capable of providing a stable fodder crop base under the changing climatic conditions of the North Kazakhstan region. The studies included analysis of air temperature and precipitation data as well as monitoring of fodder grass mixtures within a green fodder conveyor system. Different sowing dates for fodder crops and mixtures were selected for the development of the conveyor system. The range of experimental variants included fodder crops and their mixtures from various botanical families. The experiment involved both perennial (alfalfa and festulolium) and annual (corn, pea, sunflower, Sudan grass, oats, and rapeseed) crops. The highest green mass yields were achieved by the following variants: fodder crops of corn + pea—74.40 c/ha; mixtures of annual legume–grass crops in the pea + oats variant of the first sowing date—43.64 c/ha; Sudan grass + pea—45.72 c/ha; mixtures of perennial grasses in the second utilization term of alfalfa + festulolium—64.9 c/ha; and rapeseed sown at the first sowing date—46.61 c/ha. In terms of crude and digestible protein content, the best among the annual grass variants was the mixture of Sudan grass and pea (crude protein—33.59 g/kg, digestible protein—24.5 g/kg), and the best among the perennials was the variant of the first utilization term (crude protein—50.42 g/kg, digestible protein—38.2 g/kg). Regarding metabolizable energy content, the annual crop variant of corn + pea had a yield of 1.92 MJ/kg, and in the perennial variant, the mixture of alfalfa and festulolium in the first utilization term had a yield of 2.68 MJ/kg. Such an approach to creating green fodder conveyors can be crucial for developing effective strategies for adapting agriculture to climate change, including the selection of promising fodder crops and optimization of their placement. The results obtained can contribute to enhancing the productivity and sustainability of agricultural production in the North Kazakhstan region. Full article

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R² values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data. Full article

Controlling anthracnose in crops usually depends on synthetic chemicals, but essential oils offer a promising alternative with a potentially lower risk to human health and the environment. This study examines the use of noni (Morinda citrifolia L.) essential oil for preventive and curative control of anthracnose in cassava plants. Extracted from ripe noni fruit, the oil was tested at concentrations of 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, and 5.0 µL/mL for its antifungal properties against Colletotrichum species isolated from cassava. We applied the oil both preventively and curatively, monitoring for phytotoxic effects. Phytochemical analysis revealed that the main compounds were octanoic acid (64.03%), hexanoic acid (10.16%), and butanoic acid (8.64%). The oil effectively inhibited C. chrysophillum and C. musicola at 2.0 µL/mL, while C. truncatum required 5.0 µL/mL for significant inhibition. Higher concentrations reduced disease progression but showed phytotoxicity at only 5 µL/mL. Molecular docking suggested that octanoic acid interacts with the fungi’s tyrosine-tRNA ligase enzyme, hinting at its mechanism of action. Collectively, our findings reinforce the potential of noni essential oil as an alternative agent against Colletotrichum spp. in cassava crops. Full article

With the growing demand for efficient solutions to face the challenges posed by population growth and climate change, the use of unmanned aerial vehicles (UAVs) emerges as a promising solution for monitoring biophysical and physiological parameters in forage crops due to their ability to collect high-frequency and high-resolution data. This review addresses the main applications of UAVs in monitoring forage crop characteristics, in addition to evaluating advanced data processing techniques, including machine learning, to optimize the efficiency and sustainability of agricultural production systems. In this paper, the Scopus and Web of Science databases were used to identify the applications of UAVs in forage assessment. Based on inclusion and exclusion criteria, the search resulted in 590 articles, of which 463 were filtered for duplicates and 238 were selected after screening. An analysis of the data revealed an annual growth rate of 35.50% in the production of articles, evidencing the growing interest in the theme. In addition to 1086 authors, 93 journals and 4740 citations were reviewed. Finally, our results contribute to the scientific community by consolidating information on the use of UAVs in precision farming, offering a solid basis for future research and practical applications. Full article

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr⁻¹ occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil. Full article

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance. Full article

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R²) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. Full article

Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R², a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition. Full article

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems. Full article

This study proposes an improved methodology based on life cycle assessment (LCA), which is used to calculate the carbon footprint of agriculture, provides a simple and feasible calculation path, and constructs a streamlined framework for calculating the carbon footprint based on the agricultural traceability system records. Using important economic crop (Onion) as research subject, and choose the largest planting area in R.O.C. (southern Taiwan) as a case study. A total of 64 farm production history records have been collected, includes all of farms certified with a traceable agricultural products (TAP) label. Through a detailed analysis of the traditional carbon footprint calculation method, found that agricultural traceability system records could replace the data source in carbon footprint verification (CFV) process, and system records could be used as activity data after being organized. With our method, no need to go through a complicated CFV process, just download the existing data on agricultural traceability system, can start calculating carbon footprint as soon as possible. To compared to traditional assessment method, results show a margin of error is less than 6% compared to traditional assessment methods. Advantages of improved method were be found, such as easy data acquisition, simplified calculation steps, and improved data transparency and accuracy. From statistical data, show that at least seven categories of carbon emission sources for carbon footprints, the most significant of carbon emission impact are fertilizers. The result of improved methodology based on life cycle assessment (LCA), show that using the improved methods can help promote the carbon footprint management efficiency of agricultural organizations such as Farmers’ Association or Agricultural Production Marketing Group, promptly monitor the carbon footprint status of their fields and adjust strategies to reduce carbon footprints in real-time, advancing towards the goal of net-zero carbon emissions. Full article

Against the backdrop of climate change, soil loss, and water scarcity, sustainable food production is a pivotal challenge for humanity. As the global population grows and urbanization intensifies, innovative agricultural methods are crucial to meet rising food demand, while mitigating environmental degradation. Hydroponic and aquaponic systems, has emerged as one of these solutions by minimizing land use, reducing water consumption, and enabling year-round crop production in urban areas. This study aimed at assessing the yield, ecophysiological performance, and nutritional content of Lactuca sativa L. and Cichorium endivia L. var. crispum grown in hydroponic and aquaponic floating raft systems, with Oreochromis niloticus L. integrated into the aquaponic system. Both species exhibited higher fresh biomass and canopy/root ratios in hydroponics compared to aquaponics. Additionally, hydroponics increased the leaf number in curly endive by 18%. Ecophysiological parameters, such as the leaf net photosynthesis rate, actual yield of PSII, and linear electron transport rate, were also higher in hydroponics for both species. However, the nutritional profiles varied between the two cultivation systems and between the two species. Given that standard fish feed often lacks sufficient potassium levels for optimal plant growth, potassium supplementation could be a viable strategy to enhance plant development in aquaponic systems. In conclusion, although aquaponic systems may demonstrate lower productivity compared to hydroponics, they offer a more sustainable and potentially healthier product with fewer harmful compounds due to the reduced use of synthetic fertilizers, pesticides, and the absence of chemical residue accumulation. However, careful system management and monitoring are crucial to minimize potential contaminants. Full article

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO₂ (xCO₂) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO₂ is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO₂ (xCO₂), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m⁻² sr⁻¹ μm⁻¹ and 0.8 W m⁻² sr⁻¹ μm⁻¹, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t⁻¹ with low NDVI and xCO₂ values, alongside SIF values lower than 0.4 W m⁻² sr⁻¹ μm⁻¹ and greater than 1 W m⁻² sr⁻¹ μm⁻¹. These findings underscore the potential of integrating SIF, xCO₂, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops. Full article

Reniform nematode (Rotylenchulus reniformis) causes significant yield loss in cotton worldwide. In 2012, its detection in the Dawson-Callide region of Central Queensland prompted extensive surveys of cotton fields. The nematode was confirmed in 68% of sampled fields, with populations ranging from 2 to 3870 R. reniformis/200 mL of soil. Soil monitoring revealed increasing populations associated with consecutive cotton crops. However, when corn or sorghum replaced cotton, soil nematode populations significantly decreased. A two-year replicated field trial demonstrated that growing a non-host crop (such as biofumigant sorghum ‘Fumig8tor’, grain sorghum, or corn) significantly reduced nematode populations in the top 15 cm of soil compared to cotton. Unfortunately, when cotton was replanted the following season, nematode populations rebounded regardless of the previous crop. Only the ‘Fumig8tor’-cotton rotation resulted in significantly lower nematode populations than continuous cotton. Vertical soil sampling showed that rotating with a non-host crop significantly reduced nematode densities to a depth of 100 cm compared to cotton. However, when the field was replanted with cotton, nematode populations recovered, unaffected by cropping or soil depth. This study emphasises the importance of monitoring reniform nematodes in cotton soils for early detection and defining distribution patterns within a field, which may improve the effectiveness of management practices. These results suggest that one rotation out of cotton is not sufficient, as populations return to high levels when cotton is grown again. Therefore, two or more rotations out of cotton should be considered to manage this nematode. Full article

The fall armyworm (FAW), Spodoptera frugiperda, is a serious pest that threatens a range of important crops worldwide. It originated in America and rapidly dispersed throughout Africa and Asia in 2018. There are two subtypes, corn-strain (C-strain) and rice-strain (R-strain), that have different host plant preferences, and the individuals damaging maize in China were identified as C-strain. In the present study, we found FAW individuals damaging rice plants in the field of Guangdong Province, China. FAW larvae and male adults were collected, and the majority of FAWs were characterized as CO I R-strain Tpi C-strain, which is similar to the FAWs damaging maize in China. The FAW adults preferred laying eggs on maize plants more than on rice plants. Compared to those that were fed maize leaves, the FAW larvae were unable to survive when fed 4-week-old rice plants, whereas they could complete their life cycle on 2-week-old rice plants, for which the total survival rate was 8%. The pre-adult- and pupal-stage durations were prolonged, and the fecundity of adult females decreased. Thus, the FAWs found in paddy fields showed better fitness on maize than on rice in the laboratory. Owing to their low survival rate on rice plants, they were unlikely to damage paddy fields in large areas, but populations of FAWs in paddy fields should be monitored. Full article