AgriEngineering最新文献

{"title":"Carbon and Nitrogen Stocks in Topsoil under Different Land Use/Land Cover Types in the Southeast of Spain","authors":"Abderraouf Benslama, I. G. Lucas, M. J. Jordán Vidal, M. B. Almendro-Candel, J. Navarro-Pedreño","doi":"10.3390/agriengineering6010024","DOIUrl":"https://doi.org/10.3390/agriengineering6010024","url":null,"abstract":"Land use plays a crucial role in the stock of soil organic carbon (SOC) and soil nitrogen (SN). The aim of this study was to assess and characterize the effects of various soil management practices on the physicochemical properties of soil in a Mediterranean region in southeastern Spain. Texture, soil moisture, bulk density, pH, electrical conductivity, equivalent CaCO3 (%), soil organic matter and carbon, and Kjeldahl nitrogen were determined for the surface topsoil (0–5 cm, 180 samples) under three types of land cover: cropland, grassland, and urban soil. The main soil textures were silt, silt loam, and sandy loam with low percentages of soil moisture in all soil samples and lower bulk density values in cropland and grassland areas. The pH was alkaline and the electrical conductivity as well as the equivalent calcium carbonate content were moderate to high. Organic matter estimated using the LOI and WB methods varied in the order cropland > grassland > urban soil. The results obtained for SOC and SN indicate that cropland presented the highest stocks, followed by grassland and urban soil. The values determined for the C/N ratio were close to 10 in cropland and grassland, indicating that organic matter readily undergoes decomposition at these sites. Our results emphasize the importance of evaluating the effects and identifying the impacts of different soil management techniques, and further research is needed to better understand the potential to improve soil organic carbon and nitrogen storage in semiarid regions.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"52 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139782372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis","authors":"Shahab Ul Islam, Shahab Zaib, G. Ferraioli, V. Pascazio, Gilda Schirinzi, Ghassan Husnain","doi":"10.3390/agriengineering6010023","DOIUrl":"https://doi.org/10.3390/agriengineering6010023","url":null,"abstract":"Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139782635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon and Nitrogen Stocks in Topsoil under Different Land Use/Land Cover Types in the Southeast of Spain","authors":"Abderraouf Benslama, I. G. Lucas, M. J. Jordán Vidal, M. B. Almendro-Candel, J. Navarro-Pedreño","doi":"10.3390/agriengineering6010024","DOIUrl":"https://doi.org/10.3390/agriengineering6010024","url":null,"abstract":"Land use plays a crucial role in the stock of soil organic carbon (SOC) and soil nitrogen (SN). The aim of this study was to assess and characterize the effects of various soil management practices on the physicochemical properties of soil in a Mediterranean region in southeastern Spain. Texture, soil moisture, bulk density, pH, electrical conductivity, equivalent CaCO3 (%), soil organic matter and carbon, and Kjeldahl nitrogen were determined for the surface topsoil (0–5 cm, 180 samples) under three types of land cover: cropland, grassland, and urban soil. The main soil textures were silt, silt loam, and sandy loam with low percentages of soil moisture in all soil samples and lower bulk density values in cropland and grassland areas. The pH was alkaline and the electrical conductivity as well as the equivalent calcium carbonate content were moderate to high. Organic matter estimated using the LOI and WB methods varied in the order cropland > grassland > urban soil. The results obtained for SOC and SN indicate that cropland presented the highest stocks, followed by grassland and urban soil. The values determined for the C/N ratio were close to 10 in cropland and grassland, indicating that organic matter readily undergoes decomposition at these sites. Our results emphasize the importance of evaluating the effects and identifying the impacts of different soil management techniques, and further research is needed to better understand the potential to improve soil organic carbon and nitrogen storage in semiarid regions.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"38 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions","authors":"S. K. Noon, Muhammad Amjad, Muhammad Ali Qureshi, A. Mannan, Tehreem Awan","doi":"10.3390/agriengineering6010021","DOIUrl":"https://doi.org/10.3390/agriengineering6010021","url":null,"abstract":"Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture","authors":"Yara Karine de Lima Silva, C. Furlani, T. F. Canata","doi":"10.3390/agriengineering6010022","DOIUrl":"https://doi.org/10.3390/agriengineering6010022","url":null,"abstract":"The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"218 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions","authors":"S. K. Noon, Muhammad Amjad, Muhammad Ali Qureshi, A. Mannan, Tehreem Awan","doi":"10.3390/agriengineering6010021","DOIUrl":"https://doi.org/10.3390/agriengineering6010021","url":null,"abstract":"Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"114 7-8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture","authors":"Yara Karine de Lima Silva, C. Furlani, T. F. Canata","doi":"10.3390/agriengineering6010022","DOIUrl":"https://doi.org/10.3390/agriengineering6010022","url":null,"abstract":"The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels","authors":"José de Queiroz Otone, G. F. Theodoro, D. C. Santana, L. Teodoro, Job Teixeira de Oliveira, Izabela Cristina de Oliveira, C. A. da Silva Junior, P. Teodoro, F. Baio","doi":"10.3390/agriengineering6010020","DOIUrl":"https://doi.org/10.3390/agriengineering6010020","url":null,"abstract":"Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"114 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139794734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels","authors":"José de Queiroz Otone, G. F. Theodoro, D. C. Santana, L. Teodoro, Job Teixeira de Oliveira, Izabela Cristina de Oliveira, C. A. da Silva Junior, P. Teodoro, F. Baio","doi":"10.3390/agriengineering6010020","DOIUrl":"https://doi.org/10.3390/agriengineering6010020","url":null,"abstract":"Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"7 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139854533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Oil:Water Ratio in the Vertical Centrifuge Separator and Its Influence in Phenolic Compounds in the Virgin Olive Oil and the Olive Mill Wastewater (Alpechín)","authors":"Alfonso Montaño, Sofía Redondo-Redondo, Laura Moreno, Manuel Zambrano","doi":"10.3390/agriengineering6010019","DOIUrl":"https://doi.org/10.3390/agriengineering6010019","url":null,"abstract":"The use of the vertical centrifuge in the olive oil production process is generally assumed to be habitual and necessary for the elimination of both the vegetation water and the small olive pulp particles that are not eliminated during solid–liquid separation (horizontal centrifugation). Trials were carried out with different oil:water ratios to study the influence of this variable on both the quality parameters of the olive oils obtained and the loss of oil with the olive wastewater. The trials were carried out at the industrial mill level with oil:water ratios between 0.6 and 5.5. While no differences were observed in the quality parameters of the oils obtained, correct adjustment of the oil:water flow rates reduced the loss of phenols present in the oils by around 30%. In addition, the results show a direct relationship between the soluble effluent and the conductivity of the olive mill wastewater (alpechín) with the loss of oil in the effluent. This work proves that both oil quality and the competitiveness of the olive oil value chain can be increased with energy savings, water consumption reduction, and environmental sustainability.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}