Search Results (3,221)

Rosemary is a versatile Mediterranean shrub valued for its culinary and medicinal uses, also finding applications as a food additive (E-392). This study explores the potential of rosemary for large-scale cultivation as well as the valorization of its distillation residue, which constitutes more than 95% of the total biomass. Rich in bioactive compounds, this solid waste represents a valuable opportunity to develop renewable plant-based products. This study monitored the agronomic adaptations of cultivated clones of rosemary and evaluated the essential oil and phenolic content. This study also evaluated the biological potential of the ethanolic extracts from the distilled residue as an antifungal, antioxidant, chelator, and biostimulant in model tests. Interestingly, the extracts showed substantial phenolic content, exhibiting strong antifungal activity, antioxidant capacity, and efficient metal chelation. Furthermore, all extracts also demonstrated promising biostimulant effects on rooting. Among the clones evaluated, Pina de Ebro stood out especially for its balanced adaptability, high essential oil yield, and outstanding phenolic content, along with uniform biological capacities among individual plants and plots. Therefore, this study highlights the potential of utilizing the entire rosemary plant, enhancing the overall profitability of the crop and meeting the growing demand for eco-friendly and renewable resources in the market. Full article

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall’s correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, ‘Bayesian optimization with KNN’, was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices. Full article

Crop classification plays a pivotal role in agricultural remote sensing, offering critical insights into planting areas, growth monitoring, and yield evaluation. Leveraging the power of Google Earth Engine, this paper centers on the agricultural landscape of Krishna District as its study region. It explores the efficacy of multiple machine learning approaches, specifically Random Forest (RF), Classification and Regression Tree (CART), Naive Bayes, and Support Vector Machine (SVM), in composition of Sentinel-1 and Sentinel-2 satellite imagery for crop categorization. By meticulously assessing and contrasting the evaluations of these four classification methods, the results highlight the efficacy of RF. The overall accuracy (OA) regarding RF classification reaches 0.86, surpassing the results obtained by Naive Bayes (OA = 0.68), CART (OA = 0.63), and SVM (OA = 0.78). This scalable and straightforward classification methodology harnesses the advantages of cloud-based platforms for data handling and analysis. The timely and precise identification in crop typing holds immense importance for monitoring alterations in harvest patterns, estimating yields, and issuing crop safety alerts in the Krishna District and beyond. This paper contributes to the agricultural geospatial sensing domain by providing an innovative approach for accurate crop classification, with broad applications in precision farming and crop management. Full article

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models. Full article

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R² of 0.89 and RMSE of 0.050 without considering vegetation effects and an R² of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R² of 0.89 and RMSE of 6.79 without considering vegetation effects and an R² of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications. Full article

In traditional farming, fertilizers are often used without precision, resulting in unnecessary expenses and potential damage to the environment. This study introduces a new method for accurately identifying macronutrient deficiencies in Rhodena lettuce crops. We have developed a four-stage process. First, we gathered two sets of data for lettuce seedlings: one is composed of color images and the other of point clouds. In the second stage, we employed the interactive closest point (ICP) method to align the point clouds and extract 3D morphology features for detecting nitrogen deficiencies using machine learning techniques. Next, we trained and compared multiple detection models to identify potassium deficiencies. Finally, we compared the outcomes with traditional lab tests and expert analysis. Our results show that the decision tree classifier achieved 90.87% accuracy in detecting nitrogen deficiencies, while YOLOv9c attained an mAP of 0.79 for identifying potassium deficiencies. This innovative approach has the potential to transform how we monitor and manage crop nutrition in agriculture. Full article

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by $30\%$ compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under ${10}^{-3}$ in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture. Full article

Water stress (drought and waterlogging) is one highly important factor affecting food security in China. Investigating the effects of soil moisture stress on the morphological and physiological characteristics of maize seedlings is crucial for ensuring food production. The use of spectral monitoring to observe crop phenotypic traits and assess crop health has become a focal point in field crop research. However, studies exploring the contribution of crop phenotypic and physiological data to the Normalized Difference Vegetation Index (NDVI) are still limited. In this study, a 35-day pot experiment was conducted with seven soil moisture gradients: 50%, 60%, 70%, 80% (control group, CK), 90%, 100%, and 110% treatment. In order to investigate the effects of soil moisture stress on seedling phenotypes, antioxidant enzyme activities, and NDVI, an ASD FieldSpec 4 Hi-Res NG portable spectrometer was used to collect spectral data from maize (Zea mays L. B73) leaves. The contributions of maize phenotypic and physiological traits to NDVI were also examined. The results indicated that (1) the 50% and 110% treatments significantly affected maize seedling phenotypes compared to the CK group; (2) the activities of superoxide dismutase (SOD) and peroxidase (POD) in the leaves increased under water stress, while the activities of glutathione peroxidase (GSH-PX) and ascorbate peroxidase (APX) decreased; (3) soil moisture stress (drought and waterlogging) reduced photosynthetic pigments, chlorophyll content (SPAD), and NDVI, with inhibitory effects intensifying as the stress level increased; (4) Redundancy analysis showed that antioxidant enzymes explained 69.87% of the variation in seedling height, leaf area, and NDVI. Soil moisture stress, chlorophyll, and SPAD explained 58.14% of the variation in these parameters. The results demonstrated that maize seedlings were highly sensitive to soil moisture changes, and the SPAD value contributed significantly to NDVI (p < 0.01). This study provides valuable insights for future research in precision agriculture management Full article

Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83–99% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green–Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity. Full article

This study investigates the species richness and distribution of ladybird beetles (Coccinellidae) across various habitats on San Cristóbal Island in the Galápagos Archipelago, Ecuador. Through extensive field surveys, we catalogued nineteen species, including four previously known species (two endemics, Psyllobora bisigma and Scymnobius scalesius, and two natives, Cycloneda sanguinea and Tenuisvalvae bromelicola). We also identified nine possibly native species reported for the first time in the Galapagos islands in this study or correspond to the first voucher specimens for the island. We collected three previously reported non-native species: Cheilomenes sexmaculata, Novius cardinalis, and Paraneda guticollis. Three species belonging to the genera Stethorus, Calloeneis, and Delphastus remain undetermined, pending further taxonomic analyses. Our findings reveal a rich and complex community with notable differences in species abundance and habitat preference. Endemic species were found to be particularly scarce and restricted mainly to crops undergoing forest regeneration and deciduous forests, emphasising their vulnerability and specialised habitat requirements. The native Cycloneda sanguinea emerged as the most prevalent species, exhibiting broad ecological adaptability. Non-native species, like Cheilomenes sexmaculata, were predominantly found in disturbed habitats, with some showing early signs of spreading into more natural environments, raising concerns about their potential impact on local biodiversity. These findings contribute valuable knowledge to understanding Coccinellidae diversity on San Cristóbal Island and highlight the importance of continued monitoring, particularly in the face of ongoing environmental change and the introduction of non-native species. This study underscores the need for targeted conservation efforts to protect the unique and fragile ecosystems of the Galápagos Archipelago. Full article

Food security is threatened by climate change and associated abiotic stresses that affect the flowering stage and the biochemistry of flowers and fruits. In tomato, managed insect pollination and grafting elite tomato varieties onto robust rootstocks are widely practiced commercially to enhance tomato crop profitability, particularly under suboptimal conditions. However, little is known about rootstock–pollinator interactions and their impact on the chemical composition of fruit. In this study, a commercial tomato F1 hybrid (Solanum lycopersicum L.) was self-grafted and grafted onto a set of experimental rootstocks and cultivated under optimal and saline (75 mM NaCl) conditions in the presence of managed bumblebee pollinators (Bombus terrestris). The number of visits (VN) and total visiting time (TVT) by pollinators to different grafted plants were monitored through an RFID (radio-frequency identification) tracking system, while targeted metabolites (hormones, sugars, and organic and amino acids) and mineral composition were analyzed in the fruit juice by UHPLC-MS and ICP-OES, respectively. Pollinator foraging decisions were influenced by the rootstocks genotype and salinity treatment. Experimental rootstocks predominantly increased pollinator attraction compared to the self-grafted variety. Interestingly, the pollinator parameters were positively associated with the concentration of abscisic acid, salicylic acid, malate and fumarate, and tyrosine in salinized fruits. Moreover, a high accumulation of sodium was detected in the fruits of the plants most visited by pollinators, while rootstock genotype-specific responses were found for nitrogen and potassium concentrations. In addition to the known effect on yield, these findings underscore the synergic interactions between rootstocks, pollinators, and environmental stressors on tomato fruit composition. Full article

Pesticides play a critical role in modern agriculture by protecting crops and ensuring higher yields, but their widespread use raises concerns about human health and environmental impact. Regulatory agencies impose Maximum Residue Levels (MRLs) to ensure safety, and the European Food Safety Authority (EFSA) assesses pesticide risks. This study monitored pesticide residues in 169 feed samples from Piedmont (Italy) collected between 2019 and 2023. Using GC-MS/MS, residues were found in 92% of animal-based and 70% of cereal-based feedstuffs. The most common pesticides in cereal-based feeds were pyrimiphos-methyl, deltamethrin, cypermethrin, azoxystrobin, and tetramethrin, and the pesticide synergist piperonyl-butoxide demonstrated a significant increase in contaminated samples in 2023. The lower concentrations in 2021 were likely due to COVID-19 impacts on pesticide availability. In animal-based feeds, common pesticides included deltamethrin, cypermethrin, and the pesticide synergist piperonyl-butoxide. The results highlight the pervasive presence of low-dose pesticide mixtures in feed and food chains, which could impact health, although do not pose acute risks. The study emphasizes the need for ongoing pesticide monitoring and awareness of the long-term effects of chronic pesticide exposure on animal, human, and environmental health. Full article

Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4’s novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4’s ability as an effective feature extractor for downstream semantic segmentation. Full article

Soil degradation in Europe is mostly represented by soil erosion that, as a sediment production mechanism, is the main environmental threat to many watersheds, including the Bovilla watershed (Tirana), useful for the supply of drinking water to the city, and therefore, the care of water quality is of particular interest. The soil erosion of the Bovilla watershed was monitored in a work set up in June 2017. Following this work, this research updates the previous data on soil loss at the Bovilla watershed in t/ha/year to September 2019 and focuses on the identification and monetary evaluation of the ecosystem services (ESs) linked to soil erosion (loss of carbon, loss of mineral elements, habitat quality, crop productivity, and sustainable tourism suitability). Then, we applied the replacement cost analysis to test the economic convenience and suggest the adoption of sustainable land management practices (e.g., reforestation) able to improve the quality water in this watershed. The study carried out demonstrates that the values of soil lost due to erosion vary depending on the type of land use (ranging from average values of 120.32 t/ha for bare land to values of 8.16 t/ha for wooded areas). Furthermore, from the application of monetary methods for the evaluation of some ecosystem services linked to erosion (loss of carbonaceous and mineral elements, habitat quality, productivity, suitability for sustainable tourism), it clearly emerges that the value of the productivity of agricultural crops varies from EUR 0 to 35,320.50/ha and that the service represents a more incisive service than the previous ones, so much so as to make the conversion of some agricultural land with high productivity values into wooded areas economically disadvantageous. The data from this study may help to develop Bovilla watershed management strategies for erosion and pollution control and sediment remediation mainly in agricultural lands. A program of measures can be effective for controlling soil erosion, but it must be implemented over long time frames, and it requires relevant investments from the public and private sectors, also with a view to increase the allocation of economic values of monetary compensation aimed at those who decide to start forestation projects on highly productive soils. Full article

Drought is a natural hazard that causes significant economic and human losses by creating a persistent lack of precipitation that impacts agriculture and hydrology. It has various characteristics, such as delayed effects and variability across dimensions like severity, spatial extent, and duration, making it difficult to characterize. The agricultural sector is especially susceptible to drought, which is a primary cause of crop failures and poses a significant threat to global food security. To address these risks, it is crucial to develop effective methods for identifying, classifying, and monitoring agricultural drought, thereby aiding in planning and mitigation efforts. Researchers have developed various tools, including agricultural drought indices, to quantify severity levels and determine the onset and evolution of droughts. These tools help in early-stage forecasting and ongoing monitoring of drought conditions. The field has been significantly advanced by remote sensing technology, which now offers high-resolution spatial and temporal data, improving our capacity to monitor and assess agricultural drought. Despite these technological advancements, the unpredictable nature of environmental conditions continues to pose challenges in drought assessment. It remains essential to provide an overview of agricultural drought indices, incorporating both conventional methods and modern remote sensing-based indices used in drought monitoring and assessment. Full article