Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10−21) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation.
{"title":"An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data","authors":"Hirushan Sajindra, Thilina Abekoon, Eranga M. Wimalasiri, Darshan Mehta, Upaka Rathnayake","doi":"10.3390/agriengineering5040106","DOIUrl":"https://doi.org/10.3390/agriengineering5040106","url":null,"abstract":"Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10−21) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344281","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}
Pub Date : 2023-09-29DOI: 10.3390/agriengineering5040104
Allan Lincoln Rodrigues Siriani, Isabelly Beatriz de Carvalho Miranda, Saman Abdanan Mehdizadeh, Danilo Florentino Pereira
The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.
{"title":"Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm","authors":"Allan Lincoln Rodrigues Siriani, Isabelly Beatriz de Carvalho Miranda, Saman Abdanan Mehdizadeh, Danilo Florentino Pereira","doi":"10.3390/agriengineering5040104","DOIUrl":"https://doi.org/10.3390/agriengineering5040104","url":null,"abstract":"The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199832","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}
Pub Date : 2023-09-29DOI: 10.3390/agriengineering5040105
John S. Cundiff, Robert D. Grisso, Jonathan P. Resop, John Ignosh
The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.
{"title":"Load-Out and Hauling Cost Increase with Increasing Feedstock Production Area","authors":"John S. Cundiff, Robert D. Grisso, Jonathan P. Resop, John Ignosh","doi":"10.3390/agriengineering5040105","DOIUrl":"https://doi.org/10.3390/agriengineering5040105","url":null,"abstract":"The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135245885","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}
Pub Date : 2023-09-28DOI: 10.3390/agriengineering5040103
Ramesh Bahadur Bist, Prafulla Regmi, Darrin Karcher, Yangyang Guo, Amit Kumar Singh, Casey W. Ritz, Woo Kyun Kim, Deana R. Jones, Lilong Chai
Cage-free (CF) layer houses tend to have high particulate matter (PM) levels because of bedding/litter floor and the birds’ activities, such as perching, dustbathing, and foraging on it. It has been reported that optimizing bedding management can potentially suppress PM levels in CF houses. The objectives of this study were to (1) test the effect of the top application of new bedding materials (BMs) on PM levels and (2) compare different BM PM reduction efficiencies. Small flake shavings (SFS), large flake shavings (LFS), and aspen wood chips (AWC) were top-dressed on the surface of the original litter (33-week-old litter) evenly in each of the BM treatment rooms at 20% volume of the original litter floor. The initial litter depths in the control, SFS, LFS, and AWC rooms were 4.6 ± 0.6, 4.8 ± 0.8 cm, 4.8 ± 0.8 cm, and 4.6 ± 0.9 cm, respectively. One room was used as a control without adding new BM. The results indicate that the top application of new bedding suppressed PM levels in all treatment rooms (p < 0.01). The PM2.5 reductions in the SFS, AWC, and LFS treatment rooms were 36.5%, 34.6%, and 28.9% greater than in the control room, respectively. The mitigation efficiencies were different between PM sizes. For instance, PM2.5, PM10, and TSP in the SFS room were lower than in the control room by 36.5%, 39.4%, and 38.7%, respectively. For litter quality, the moisture content was 18.0 ± 2.8, 20.0 ± 3.1, 20.6 ± 2.4, and 19.7 ± 4.2% in the control, SFS, LFS, and AWC rooms, respectively. Treatment rooms with 20% new BM had 10% higher litter moisture than the control room. The findings of this study reveal that the top application of new bedding on old litter is a potential strategy for reducing PM generation in CF houses. Further studies are warranted, such as regarding the effect of different ratios of new bedding on PM reduction, cost analysis, and verification tests in commercial CF houses.
{"title":"Bedding Management for Suppressing Particulate Matter in Cage-Free Hen Houses","authors":"Ramesh Bahadur Bist, Prafulla Regmi, Darrin Karcher, Yangyang Guo, Amit Kumar Singh, Casey W. Ritz, Woo Kyun Kim, Deana R. Jones, Lilong Chai","doi":"10.3390/agriengineering5040103","DOIUrl":"https://doi.org/10.3390/agriengineering5040103","url":null,"abstract":"Cage-free (CF) layer houses tend to have high particulate matter (PM) levels because of bedding/litter floor and the birds’ activities, such as perching, dustbathing, and foraging on it. It has been reported that optimizing bedding management can potentially suppress PM levels in CF houses. The objectives of this study were to (1) test the effect of the top application of new bedding materials (BMs) on PM levels and (2) compare different BM PM reduction efficiencies. Small flake shavings (SFS), large flake shavings (LFS), and aspen wood chips (AWC) were top-dressed on the surface of the original litter (33-week-old litter) evenly in each of the BM treatment rooms at 20% volume of the original litter floor. The initial litter depths in the control, SFS, LFS, and AWC rooms were 4.6 ± 0.6, 4.8 ± 0.8 cm, 4.8 ± 0.8 cm, and 4.6 ± 0.9 cm, respectively. One room was used as a control without adding new BM. The results indicate that the top application of new bedding suppressed PM levels in all treatment rooms (p < 0.01). The PM2.5 reductions in the SFS, AWC, and LFS treatment rooms were 36.5%, 34.6%, and 28.9% greater than in the control room, respectively. The mitigation efficiencies were different between PM sizes. For instance, PM2.5, PM10, and TSP in the SFS room were lower than in the control room by 36.5%, 39.4%, and 38.7%, respectively. For litter quality, the moisture content was 18.0 ± 2.8, 20.0 ± 3.1, 20.6 ± 2.4, and 19.7 ± 4.2% in the control, SFS, LFS, and AWC rooms, respectively. Treatment rooms with 20% new BM had 10% higher litter moisture than the control room. The findings of this study reveal that the top application of new bedding on old litter is a potential strategy for reducing PM generation in CF houses. Further studies are warranted, such as regarding the effect of different ratios of new bedding on PM reduction, cost analysis, and verification tests in commercial CF houses.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387520","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}
Varroa destructor infestation is a major factor leading to the global decline of honeybee populations. Monitoring the level of Varroa mite infestation in order to take timely control measures is crucial for the protection of bee colonies. Machine vision systems can achieve non-invasive Varroa mite detection on bee colonies, but it is challenged by two factors: the complex dynamic scenes of honeybees and small-scale and limited data on Varroa destructor. We design a convolutional neural network integrated with machine vision to solve these problems. To address the first challenge, we separate the image of the honeybee from its surroundings using a segmentation network, and the object-detection network YOLOX detects Varroa mites within the segmented regions. This collaboration between segmentation and object detection allows for more precise detection and reduces false positives. To handle the second challenge, we add a Coordinate Attention (CA) mechanism in YOLOX to extract a more discriminative representation of Varroa destructor and improve the confidence loss function to alleviate the problem of class imbalance. The experimental results in the bee farm showed that the evaluation metrics of our model are better than other models. Our network’s detection value for the percentage of honeybees infested with Varroa mites is 1.13%, which is the closest to the true value of 1.19% among all the detection values.
{"title":"Detection of Varroa destructor Infestation of Honeybees Based on Segmentation and Object Detection Convolutional Neural Networks","authors":"Mochen Liu, Mingshi Cui, Baohua Xu, Zhenguo Liu, Zhenghao Li, Zhenyuan Chu, Xinshan Zhang, Guanlu Liu, Xiaoli Xu, Yinfa Yan","doi":"10.3390/agriengineering5040102","DOIUrl":"https://doi.org/10.3390/agriengineering5040102","url":null,"abstract":"Varroa destructor infestation is a major factor leading to the global decline of honeybee populations. Monitoring the level of Varroa mite infestation in order to take timely control measures is crucial for the protection of bee colonies. Machine vision systems can achieve non-invasive Varroa mite detection on bee colonies, but it is challenged by two factors: the complex dynamic scenes of honeybees and small-scale and limited data on Varroa destructor. We design a convolutional neural network integrated with machine vision to solve these problems. To address the first challenge, we separate the image of the honeybee from its surroundings using a segmentation network, and the object-detection network YOLOX detects Varroa mites within the segmented regions. This collaboration between segmentation and object detection allows for more precise detection and reduces false positives. To handle the second challenge, we add a Coordinate Attention (CA) mechanism in YOLOX to extract a more discriminative representation of Varroa destructor and improve the confidence loss function to alleviate the problem of class imbalance. The experimental results in the bee farm showed that the evaluation metrics of our model are better than other models. Our network’s detection value for the percentage of honeybees infested with Varroa mites is 1.13%, which is the closest to the true value of 1.19% among all the detection values.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719253","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}
The survival of newly planted seedlings and their successful development after transplantation, including faster plant growth, improved plant quality, larger production, and the absence of dependence on arable land, is one of the primary goals of horticultural nurseries. Although peat is the most frequently used amendment in commercial potting substrates, exploiting it degrades essential ecosystems like peatlands and uses slowly renewable resources. This study evaluated the growth and nutrition of olive-rooted cuttings when peat was partially or completely replaced with vermicompost, searching for more sustainable methods and recovering urban wastewater treatment sludge sequentially. The progress of the plants’ growth was compared to that of corresponding plants in which commercial peat had been used as substrate. Leachates from every procedure were also examined, and the results revealed that trace element and heavy metal contents were much lower than those deemed hazardous for aquifers and soil. The outcomes indicated that peat might be effectively replaced with vermicompost sludge, promoting plant growth without further fertilizer. Comparatively to olive cuttings grown in peat-based substrates, those grown in compost-based substrates experienced improved nutrition and development. Further, it was found that irrigation doses were significantly reduced in treatments with a significant amount of vermicompost as the water drained more slowly. A technical-economic analysis was being conducted in the meantime, illustrating the financial benefits for a nursery when peat is replaced with vermicomposted sludge.
{"title":"Utilization of Vermicompost Sludge Instead of Peat in Olive Tree Nurseries in the Frame of Circular Economy and Sustainable Development","authors":"Vasiliki Kinigopoulou, Evangelos Hatzigiannakis, Stefanos Stefanou, Athanasios Guitonas, Efstathios K. Oikonomou","doi":"10.3390/agriengineering5030101","DOIUrl":"https://doi.org/10.3390/agriengineering5030101","url":null,"abstract":"The survival of newly planted seedlings and their successful development after transplantation, including faster plant growth, improved plant quality, larger production, and the absence of dependence on arable land, is one of the primary goals of horticultural nurseries. Although peat is the most frequently used amendment in commercial potting substrates, exploiting it degrades essential ecosystems like peatlands and uses slowly renewable resources. This study evaluated the growth and nutrition of olive-rooted cuttings when peat was partially or completely replaced with vermicompost, searching for more sustainable methods and recovering urban wastewater treatment sludge sequentially. The progress of the plants’ growth was compared to that of corresponding plants in which commercial peat had been used as substrate. Leachates from every procedure were also examined, and the results revealed that trace element and heavy metal contents were much lower than those deemed hazardous for aquifers and soil. The outcomes indicated that peat might be effectively replaced with vermicompost sludge, promoting plant growth without further fertilizer. Comparatively to olive cuttings grown in peat-based substrates, those grown in compost-based substrates experienced improved nutrition and development. Further, it was found that irrigation doses were significantly reduced in treatments with a significant amount of vermicompost as the water drained more slowly. A technical-economic analysis was being conducted in the meantime, illustrating the financial benefits for a nursery when peat is replaced with vermicomposted sludge.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135063653","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}
Pub Date : 2023-09-15DOI: 10.3390/agriengineering5030100
Patrick Bezerra Fernandes, Camila Alves dos Santos, Antonio Leandro Chaves Gurgel, Lucas Ferreira Gonçalves, Natália Nogueira Fonseca, Rafaela Borges Moura, Kátia Aparecida de Pinho Costa, Tiago do Prado Paim
The quantification of forage availability in tropical grasses is generally done in a destructive and time-consuming manner, involving cutting, weighing, and waiting for drying. To expedite this process, non-destructive methods can be used, such as unmanned aerial vehicles (UAVs) equipped with high-definition cameras, mobile device images, and the use of the normalized difference vegetation index (NDVI). However, these methods have been underutilized in tropical pastures. A literature review was conducted to present the current state of remote tools’ use in predicting forage availability and quality in tropical pastures. Few publications address the use of non-destructive methods to estimate forage availability in major tropical grasses (Megathyrsus maximus; Urochloa spp.). Additionally, these studies do not consider the fertility requirements of each cultivar and the effect of management on the phenotypic plasticity of tillers. To obtain accurate estimates of forage availability and properly manage pastures, it is necessary to integrate remote methods with in situ collection of soil parameters. This way, it will be possible to train machine learning models to obtain precise and reliable estimates of forage availability for domestic ruminant production.
{"title":"Non-Destructive Methods Used to Determine Forage Mass and Nutritional Condition in Tropical Pastures","authors":"Patrick Bezerra Fernandes, Camila Alves dos Santos, Antonio Leandro Chaves Gurgel, Lucas Ferreira Gonçalves, Natália Nogueira Fonseca, Rafaela Borges Moura, Kátia Aparecida de Pinho Costa, Tiago do Prado Paim","doi":"10.3390/agriengineering5030100","DOIUrl":"https://doi.org/10.3390/agriengineering5030100","url":null,"abstract":"The quantification of forage availability in tropical grasses is generally done in a destructive and time-consuming manner, involving cutting, weighing, and waiting for drying. To expedite this process, non-destructive methods can be used, such as unmanned aerial vehicles (UAVs) equipped with high-definition cameras, mobile device images, and the use of the normalized difference vegetation index (NDVI). However, these methods have been underutilized in tropical pastures. A literature review was conducted to present the current state of remote tools’ use in predicting forage availability and quality in tropical pastures. Few publications address the use of non-destructive methods to estimate forage availability in major tropical grasses (Megathyrsus maximus; Urochloa spp.). Additionally, these studies do not consider the fertility requirements of each cultivar and the effect of management on the phenotypic plasticity of tillers. To obtain accurate estimates of forage availability and properly manage pastures, it is necessary to integrate remote methods with in situ collection of soil parameters. This way, it will be possible to train machine learning models to obtain precise and reliable estimates of forage availability for domestic ruminant production.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437930","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}
Pub Date : 2023-09-13DOI: 10.3390/agriengineering5030099
María Ángeles Peñaranda Barba, Virginia Alarcón Martínez, Ignacio Gómez Lucas, Jose Navarro-Pedreño
Open pit mining causes damage in natural and rural regions; that is why soil restoration is necessary in order to recovery soil–plant systems. The application of waste can be a good solution for rehabilitation, and it clearly complies with the circular economy and the zero-waste strategy. This study was carried out in a quarry restoration area in the southeast of Spain, where experimental plots were designed and fertilized with different amendments (commonly used inorganic fertilizer N-K-P, pig slurry, pruning waste and urban solid wastes) with the objective of studying ways to improve the restoration of the soil by using these residues and increase the soil fertility before planting. The treatments applied were evaluated in the short term (two and four months from their addition to topsoil) and medium term (nine months) in order to determine if the restored soils will be adequate for agriculture based on nutrients’ availability. The results showed that in all the treatments, the pH exceeded 8.5 due to the nature of the soil matrix, but after 9 months of the application, in the plots treated with NPK and pig slurry, the pH decreased. In general, with the application of the treatments, soil macro- (N, P, K, Na, Ca and Mg) and micro-nutrients (Fe and Cu) were increased. However, pig slurry and urban solid waste favored N and P, respectively.
{"title":"Restoration Techniques Applied in Open Mining Area to Improve Agricultural Soil Fertility","authors":"María Ángeles Peñaranda Barba, Virginia Alarcón Martínez, Ignacio Gómez Lucas, Jose Navarro-Pedreño","doi":"10.3390/agriengineering5030099","DOIUrl":"https://doi.org/10.3390/agriengineering5030099","url":null,"abstract":"Open pit mining causes damage in natural and rural regions; that is why soil restoration is necessary in order to recovery soil–plant systems. The application of waste can be a good solution for rehabilitation, and it clearly complies with the circular economy and the zero-waste strategy. This study was carried out in a quarry restoration area in the southeast of Spain, where experimental plots were designed and fertilized with different amendments (commonly used inorganic fertilizer N-K-P, pig slurry, pruning waste and urban solid wastes) with the objective of studying ways to improve the restoration of the soil by using these residues and increase the soil fertility before planting. The treatments applied were evaluated in the short term (two and four months from their addition to topsoil) and medium term (nine months) in order to determine if the restored soils will be adequate for agriculture based on nutrients’ availability. The results showed that in all the treatments, the pH exceeded 8.5 due to the nature of the soil matrix, but after 9 months of the application, in the plots treated with NPK and pig slurry, the pH decreased. In general, with the application of the treatments, soil macro- (N, P, K, Na, Ca and Mg) and micro-nutrients (Fe and Cu) were increased. However, pig slurry and urban solid waste favored N and P, respectively.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783072","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}
Pub Date : 2023-09-13DOI: 10.3390/agriengineering5030098
Jean J. Novais, Raúl R. Poppiel, Marilusa P. C. Lacerda, José A. M. Demattê
Tropical climate conditions favor landscape evolution and the formation of highly weathered soils under different pedogenic processes due to certain differential properties. Traditional analysis coupled with VNIR-SWIR reflectance spectroscopy and X-ray diffractometry (XRD) analyses can reveal such characteristics. Several researchers cited throughout this study already discussed the possible applications of analyses in this field. All agree that integrated knowledge (holistic) can drive the future of the soil sciences. However, few refer to the potential of soil spectroscopy in deriving pedogenetic information. Thus, this paper aimed to assess pedomorphogeological relationships in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. We performed landscape observations and soil sampling in the field. The laboratory’s physical, chemical, spectral, and mineralogical determinations supported the soil classification according to the World Reference Basis (WRB/FAO) system. Based on the analysis results, we divided five profiles into two soil groups (highly and slightly weathered soils) using Pearson’s correlation and hierarchical clustering analysis (HCA). Traditional analyses determined the diagnostic attributes. Spectroscopic readings from 0.35 to 2.5 µm wavelengths and XRD supported identifying soil attributes and properties. Finally, all soil classes were correlated according to correspondent reflectance spectra and primary pedological attributes. There was a strong correlation between spectral oxide features and X-ray diffraction peaks. The HCA based on oxide content and mineral composition validated the previous soil grouping. Thus, we could assess the pedomorphogeological relationships through VNIR-SWIR spectroscopy, XRD, and traditional analyses concerning pedogenic processes through their correlation with soil properties resulting from these processes. However, periodic measurements are required, making orbital sensing a continuous data source for soil monitoring.
热带气候条件在不同成土过程下有利于景观演化和高风化土的形成。传统的分析结合VNIR-SWIR反射光谱和x射线衍射(XRD)分析可以揭示这些特征。本研究中引用的几位研究人员已经讨论了分析在该领域的可能应用。所有人都同意，综合知识(整体)可以推动土壤科学的未来。然而，很少有人提到土壤光谱学在获得成土信息方面的潜力。因此，本文旨在利用传统分析和地质技术来评估巴西中西部具有代表性的地形序列中的土壤形态地质关系。我们在野外进行了景观观察和土壤取样。实验室的物理、化学、光谱和矿物学测定支持了根据世界参考基础(WRB/FAO)系统进行的土壤分类。基于分析结果，采用Pearson’s correlation and hierarchical clustering analysis (HCA)方法将5个剖面划分为两个土壤类群(高风化土和轻度风化土)。传统的分析确定了诊断属性。光谱读数从0.35至2.5µm波长和XRD支持识别土壤的属性和性质。最后，根据各土壤类别对应的反射光谱和主要土壤学属性进行相关性分析。光谱特征与x射线衍射峰之间有很强的相关性。基于氧化物含量和矿物组成的HCA验证了之前的土壤分组。因此，我们可以通过VNIR-SWIR光谱，XRD和传统的成土过程分析来评估土壤形态地质关系，通过它们与这些过程产生的土壤性质的相关性来评估成土过程。然而，需要周期性的测量，使轨道传感成为土壤监测的连续数据源。
{"title":"VNIR-SWIR Spectroscopy, XRD and Traditional Analyses for Pedomorphogeological Assessment in a Tropical Toposequence","authors":"Jean J. Novais, Raúl R. Poppiel, Marilusa P. C. Lacerda, José A. M. Demattê","doi":"10.3390/agriengineering5030098","DOIUrl":"https://doi.org/10.3390/agriengineering5030098","url":null,"abstract":"Tropical climate conditions favor landscape evolution and the formation of highly weathered soils under different pedogenic processes due to certain differential properties. Traditional analysis coupled with VNIR-SWIR reflectance spectroscopy and X-ray diffractometry (XRD) analyses can reveal such characteristics. Several researchers cited throughout this study already discussed the possible applications of analyses in this field. All agree that integrated knowledge (holistic) can drive the future of the soil sciences. However, few refer to the potential of soil spectroscopy in deriving pedogenetic information. Thus, this paper aimed to assess pedomorphogeological relationships in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. We performed landscape observations and soil sampling in the field. The laboratory’s physical, chemical, spectral, and mineralogical determinations supported the soil classification according to the World Reference Basis (WRB/FAO) system. Based on the analysis results, we divided five profiles into two soil groups (highly and slightly weathered soils) using Pearson’s correlation and hierarchical clustering analysis (HCA). Traditional analyses determined the diagnostic attributes. Spectroscopic readings from 0.35 to 2.5 µm wavelengths and XRD supported identifying soil attributes and properties. Finally, all soil classes were correlated according to correspondent reflectance spectra and primary pedological attributes. There was a strong correlation between spectral oxide features and X-ray diffraction peaks. The HCA based on oxide content and mineral composition validated the previous soil grouping. Thus, we could assess the pedomorphogeological relationships through VNIR-SWIR spectroscopy, XRD, and traditional analyses concerning pedogenic processes through their correlation with soil properties resulting from these processes. However, periodic measurements are required, making orbital sensing a continuous data source for soil monitoring.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741756","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}
Pub Date : 2023-09-12DOI: 10.3390/agriengineering5030097
Artur Gafurov
Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes.
{"title":"Automated Mapping of Cropland Boundaries Using Deep Neural Networks","authors":"Artur Gafurov","doi":"10.3390/agriengineering5030097","DOIUrl":"https://doi.org/10.3390/agriengineering5030097","url":null,"abstract":"Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135886208","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}
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