Search Results (31)

Cotton represents one of the world’s most significant agricultural commodities. However, severe weed proliferation in cotton fields seriously hampers the development of the cotton industry, making precise weed control essential for ensuring healthy cotton growth. Traditional object detection methods often suffer from computational complexity, rendering them difficult to deploy on resource-constrained edge devices. To address this challenge, this paper proposes AVGS-YOLO, a lightweight and enhanced model employing a Quadruple Synergistic Lightweight Perception Mechanism (QSLPM) for precise weed detection in complex cotton field environments. The QSLPM emphasizes synergistic interactions between modules. It integrates lightweight neck architecture (Slimneck) to optimize feature extraction pathways for cotton weeds; the ADown module (Adaptive Downsampling) replaces Conv modules to address model parameter redundancy; the small object attention modulation module (SEAM) enhances the recognition of small-scale cotton weed features; and angle-sensitive geometric regression (SIoU) improves bounding box localization accuracy. Experimental results demonstrate that the AVGS-YOLO model achieves 95.9% precision, 94.2% recall, 98.2% mAP50, and 93.3% mAP50-95. While maintaining high detection accuracy, the model achieves a lightweight design with reductions of 17.4% in parameters, 27% in GFLOPs, and 14.5% in model size. Demonstrating strong performance in identifying cotton weeds within complex cotton field environments, this model provides technical support for deployment on resource-constrained edge devices, thereby advancing intelligent agricultural development and safeguarding the healthy growth of cotton crops. Full article

Coffee is among the primary agricultural commodities in international trade; however, mapping coffee crops in mountainous regions faces limitations due to high spectral variability and complex canopy structures. This study hypothesized that optimized spectral band combinations focused on the visible spectrum may outperform configurations including near-infrared (NIR) for coffee crop segmentation. This work aimed to evaluate how different spectral band combinations affect the performance of the U-Net for segmenting coffee crops in mountainous regions. Seven PlanetScope images (4 m resolution) from Matas de Minas, Brazil, covering different phenological stages in 2023–2024, were divided into 316 training patches and 25 test patches of 256 × 256 pixels and used to train U-Net models across five spectral band combinations: (B, G, R), (B, G, NIR), (B, R, NIR), (G, R, NIR), and (B, G, R, NIR). The visible spectrum combination (B, G, R) demonstrated superior performance with an overall Accuracy of 0.8669 and, for the Coffee Crops class, an F1-score of 0.8682 and an IoU of 0.7671, outperforming all NIR-inclusive configurations. Visible bands’ sensitivity to pigmentation variations proved more effective in heterogeneous environments, while NIR increased spectral confusion near native vegetation and crop edges. The model overestimated cultivated area by 18.3% due to mixed pixels from 4 m resolution and mountainous terrain. These findings confirm that visible-spectrum bands offer a cost-effective alternative for coffee segmentation, though higher spatial resolution is needed for improved boundary delineation. Full article

Coffee, as a perennial commodity crop, plays a crucial role in global agricultural markets, regional livelihoods, and poverty alleviation. Yunnan Province of China (21°8′–29°15′N) represents the northernmost coffee-growing region worldwide, and its production has gained increasing attention in international markets. However, the absence of a spatially explicit and high-resolution coffee distribution dataset has constrained environmental assessment, land-use analysis, and policy-making in this subtropical and marginal growing region. In this study, we developed the first 10 m resolution Arabica coffee distribution dataset for Yunnan Province for the year 2023 using Sentinel-2 optical imagery and Shuttle Radar Topographic Mission (SRTM) terrain data within the Google Earth Engine (GEE) platform. An object-based workflow was implemented to generate spatially coherent mapping units, followed by supervised classification to identify coffee plantations. The resulting map achieved an overall accuracy (OA) of 0.87, with user accuracy (UA), producer accuracy (PA), and F1 score of 0.90, 0.96, and 0.93 for the coffee class, demonstrating its reliability for regional-scale applications. Feature contribution analysis indicates that shortwave infrared (SWIR) and red-edge information, particularly during the dry season, plays an important role in coffee discrimination. These results enhance confidence in the ecological relevance and stability of the mapping framework. The proposed workflow provides a practical and transferable approach for perennial crop mapping in complex mountainous environments. More importantly, the generated high-resolution coffee distribution dataset establishes a spatial baseline for monitoring land-use dynamics, assessing ecological impacts, and supporting sustainable coffee development in southwestern China. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

Banana (Musa spp.) crops face severe yield and economic losses due to foliar diseases such as Moko disease and Black Sigatoka. In Ecuador, Moko outbreaks have increasingly devastated banana plantations, threatening one of the country’s most important export commodities and putting significant pressure on local producers and the national economy. Traditional field inspection methods are labor-intensive, subjective, and often ineffective for timely disease detection and containment. In this study, we propose an improved deep learning-based segmentation approach using YOLOv8 architectures to automatically detect and segment Moko and Black Sigatoka infections from unmanned aerial vehicle (UAV) imagery. Multiple YOLOv8 configurations were systematically analyzed and compared, including variations in backbone depth, model size, and hyperparameter tuning, to identify the most robust setup for field conditions. The final optimized configuration achieved a mean precision of 79.6%, recall of 80.3%, mAP@0.5 of 84.9%, and mAP@0.5:0.95 of 62.9%. The experimental results demonstrate that the improved YOLOv8 segmentation model significantly outperforms previous classification-based methods, offering precise instance-level localization of disease symptoms. This study provides a solid foundation for developing UAV-based automated monitoring pipelines, contributing to more efficient, objective, and scalable disease management strategies. Full article

This study analyzes the potential utilization of rock dust as a sustainable alternative to the use of traditional fertilizers, considering the distance between mining areas and areas where agricultural commodities are produced in the state of Mato Grosso, the largest agricultural producing state in Brazil. To this end, agricultural production by municipality and the position of mining areas in the mining phase related to granite, basalt and kimberlite are analyzed, aiming at developing a map of current potential areas of rock dust fertilizer production, namely for phosphorus (P)- and potassium (K)-based rocks for crops. To analyze the future scenario, areas in the research phase for the same types of rocks mentioned are considered. The results indicate three main potential scenarios: (1) municipalities located in areas that produce agricultural commodities and far from mining areas in production; (2) municipalities located near areas that produce rock dust and with high agricultural production of commodities; (3) municipalities near areas that produce rock dust, but with low production of agricultural commodities. In scenario 1, the use of rock dust may be viable in the event of a supply crisis of traditional fertilizer. In scenario 2, rock dust may fully or partially replace traditional fertilizer. And in scenario 3, rock dust may be used to reduce costs and improve the production of small local producers. Thus, this study indicates that rock dust can be an alternative to traditional fertilizers in the state of Mato Grosso, but it requires registrations in accordance with Brazilian legislation. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area’s six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. Full article

In recent years, the primary sector in Italy and elsewhere has been profoundly affected by climate change and a deep economic crisis, mainly linked to stagnating prices and rising production costs. Because of this situation, we are witnessing renewed interest in alternative agricultural productions, which are characterized by their resilience and sustainability, including medicinal and aromatic plants (MAPs). This sector is characterized by a certain heterogeneity due to the great variety of species and their wide range of uses. Although these characteristics contribute to the sector’s economic success, they also hinder its study due to commodity complexity and limited data availability. At the farm level, the situation is complicated by the fact that MAP cultivation is often embedded in complex cropping systems, and more rarely, is practiced exclusively or predominantly. In light of these considerations, we concentrated solely on the agricultural phase of the supply chain, using data available in the Farm Accountancy Data Network. We aimed to examine the main structural characteristics and economic outcomes of Italian farms that grow MAP, as well as the profitability of some of the species. To ensure accurate species classification, only MAPs exclusively designated for botanical use in the Italian National List were considered. The analysis of farm economic performance indicators (gross output, variable costs, gross margins, etc.) focused mainly on the species most represented in the sample: saffron, rosemary, lavender, oregano, and sage. The results indicate that the total gross output and gross margin show the best performance in the case of saffron (66,200 and 57,600 EUR/ha, respectively) and rosemary (27,500 and 22,000 EUR/ha, respectively). However, for saffron, the biggest cost concerns propagation (purchase of bulbs), amounting to 50% of the variable costs, whereas fertilization ones are particularly high for sage and rosemary. Full article

Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately leading to increased profitability in crop production. This technology is becoming a reality in coffee farming, an essential commodity in the global economic balance, mainly due to academic attention and applicability. This study presents a bibliometric analysis focused on using RPASs in coffee farming to structure the existing academic literature and reveal trends and insights into the research topic. For this purpose, searches were conducted over the last 20 years (2002 to 2022) in the Web of Science and Scopus scientific databases. Subsequently, bibliometric analysis was applied using Biblioshiny for Bibliometrix software in R (version 2022.07.1), with emphasis on the temporal evolution of research on the topic, performance analysis highlighting key publications, journals, researchers, institutions, countries, and the scientific mapping of co-authorship, keywords, and future trends/possibilities. The results revealed 42 publications on the topic, with the pioneering studies being the most cited. Brazilian researchers and institutions (Federal University of Lavras) have a strong presence in publications on the subject and in journals focusing on technological applications. As future trends and possibilities, the employment of technology optimizes the productivity and profitability studies of coffee farming for the timely and efficient application of aerial imaging. Full article

Seed coat colour is an important quality trait, domestication trait, and morphological marker, and it is closely associated with flavonoid and anthocyanin metabolism pathways. The seed coat colour of the adzuki bean, an important legume crop, influences the processing quality, the commodity itself, and its nutritional quality. In this review, a genetic analysis of different seed coat colours, gene mapping, metabolite content determination, and varietal improvement in adzuki bean are summarized. It provides further insight into gene mapping and cloning of seed coat colour genes and varietal improvements in adzuki beans. Full article

Understanding long-term agricultural productivity is essential for designing agricultural policies, planning and targeting other economic policies (e.g., industrial policy), and managing agricultural business models. In a developing and oil-rich country such as Azerbaijan, agriculture is among the limited opportunities to diversify oil-based value added and address broad welfare issues, as farmers and agricultural workers account for a large share of total employment and the labor force. However, previous studies have not focused on an empirical assessment of the long-term and subsectoral productivity of crop commodities. Rather, they have used a highly aggregated and short-run perspective, focusing mainly on the impact of the oil sector on agricultural sectors. Here, we applied principal component analysis and hierarchical cluster analysis to identify similarities and differences in the productivity of specific crop commodities (e.g., cotton, tea, grains, tobacco, hay, fruits, and vegetables) between 1950 and 2021. We show that some crops are similar in terms of their variation, growth rates, and transition from the Soviet era to the post-Soviet period. Although the dynamics of change are different for food and non-food crops and for high- and low-productive commodities, it is still possible to narrow down specific subsectors that could reach the same productivity levels. This helps map out the productivity levels of crop commodities over time and across different subsectors, allowing for better policy decisions and resource allocation in the agricultural sector. In addition, we argue about some outlier commodities and their backward status despite extensive government support. Our results provide a common basis for policymakers and businesses to focus specifically on productivity and profitability from an economic standpoint. Full article

Sugar is a significant commodity for Indonesia because the need for sugar reaches 7 million tons. Meanwhile, imports from Thailand, Australia, and Brazil were approximately 5.54 million tons in 2020. Sugarcane and sugar production in East Java province is also supported by Lamongan Regency. Therefore, this study aims to evaluate the possible sugarcane plantation expansion areas in Lamongan. The evaluation process carried out in this study was an analysis of land suitability using the analytic network process (ANP) and land availability using an overlay analysis of several policy maps. Three parameters with the highest weight of the ANP were soil drainage (0.181), cation exchange capacity and base saturation (0.134), and rainfall (0.133). The total possible area for sugarcane plantations expansion in Lamongan was 32,552.37 ha and the largest class was Possible Area 2 (65.67%). The three sub-districts with the highest possible areas include Solokuro, Ngimbang, and Mantup. We recommend that the government and stakeholders extend the area allocated to sugarcane plantations in Lamongan because the possible expansion areas are still more than 30 ha, while in the 2011–2031 spatial plan they were only 8927 ha. Expansion plans must take into consideration other uses such as residence, industry, food crops, and protected areas. Full article

The flowrate control of spraying systems with pulse-width-modulated solenoid valves is currently being implemented for precision herbicide application in commodity crops, but solutions for fruit trees set in orchards that require higher pressures are mostly in the development stage. A reason for this has been the higher flowrate and pressure requirements of blast sprayers used for dense canopies typical of high value crops. In the present study, the duty cycles preset by an operator were compared to the actual ones estimated from measuring flowrates. A new developed air-assisted orchard sprayer with shelf hollow disc-cone nozzles was studied, such that flowrates and pressures were registered by a computer for different duty cycles commanded by an operator from 10% to 100% in intervals of 10%. In addition to sensor data, visual assessment was carried out via high-speed video images. The results showed that preset duty cycles were always more than 10% lower than the actual DC estimated from measured flowrates. The effective operational range of the duty cycles went from 20% to 80%. In general, the deviations in transitional periods were higher for lower duty cycles, being difficult to determine the real reduction in flowrate during the transition periods. A correction model has been proposed to adjust the preset duty cycles to make sure that the necessary spray flowrate is released as precisely commanded by prescription maps. Further research will be needed to verify the proper implementation of the developed correction model in field applications. Full article

Rice is one of the most essential and strategic food sources globally. Accordingly, policymakers and planners often consider a special place in the agricultural economy and economic development for this essential commodity. Typically, a sample survey is carried out through field observations and farmers’ consultations to estimate annual rice yield. Studies show that these methods lead to many errors and are time-consuming and costly. Satellite remote sensing imagery is widely used in agriculture to provide timely, high-resolution data and analytical capabilities. Earth observations with high spatial and temporal resolution have provided an excellent opportunity for monitoring and mapping crop fields. This study used the time series of dual-pol synthetic aperture radar (SAR) images of Sentinel-1 and multispectral Sentinel-2 images from Sentinel-1 and Sentinel-2 ESA’s Copernicus program to extract rice cultivation areas in Mazandaran province in Iran. A novel multi-channel streams deep feature extraction method was proposed to simultaneously take advantage of SAR and optical imagery. The proposed framework extracts deep features from the time series of NDVI and original SAR images by first and second streams. In contrast, the third stream integrates them into multi-levels (shallow to deep high-level features); it extracts deep features from the channel attention module (CAM), and group dilated convolution. The efficiency of the proposed method was assessed on approximately 129,000 in-situ samples and compared to other state-of-the-art methods. The results showed that combining NDVI time series and SAR data can significantly improve rice-type mapping. Moreover, the proposed methods had high efficiency compared with other methods, with more than 97% overall accuracy. The performance of rice-type mapping based on only time-series SAR images was better than only time-series NDVI datasets. Moreover, the classification performance of the proposed framework in mapping the Shirodi rice type was better than that of the Tarom type. Full article