AgriEngineering最新文献

{"title":"Agricultural Tractor Test: A Bibliometric Review","authors":"K. P. Lanças, A. C. Marques Filho, Lucas Santos Santana, G. Ferraz, R. O. Faria, Murilo Ba tt istuzzi Martins","doi":"10.3390/agriengineering6030131","DOIUrl":"https://doi.org/10.3390/agriengineering6030131","url":null,"abstract":"Agricultural tractors are an essential agricultural power source. Therefore, the scientific literature tests have described agricultural tractors’ evolution over time and determined future trends. This paper uses bibliometric tools to assess the agricultural evolution of tractor testing from 1969 to 2022 to ascertain the publication’s scientific perspective on operational, ergonomic, and energy performance. We searched for relevant research in the Scopus and Web of Science (WOS) databases. The data were processed in RStudio software version 4.4.1, and we used elaborated bibliometric maps to research evolution, major journals, studies, countries, and keywords. The first research mainly concerned the development of new wheelsets, more efficient engines, and fuel consumption prediction models. After the 2000s, environmental protocols contributed to increasing publications on biofuels and renewable energies. Recently, an intense process of robotization in autonomous vehicles has improved to allow the replacement of combustion engines. Ergonomics and safety have been less recurrent topics in recent years, indicating a stable level in the actual research. New machine control models involving artificial intelligence are currently applied to obtain test results without using the machine in the field. These virtual models reduce costs and optimize resources. The most common terms were “tractor” and “agricultural machinery”. The terms “Electric tractor”, “agricultural robots”, and “Matlab” indicate solid trends for future research.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830466","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":"Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data","authors":"Mohamed Mouafik, Mounir Fouad, Ahmed El Aboudi","doi":"10.3390/agriengineering6030134","DOIUrl":"https://doi.org/10.3390/agriengineering6030134","url":null,"abstract":"In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R2 value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R2 of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829916","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":"X-ray Microtomography Analysis of Integrated Crop–Livestock Production’s Impact on Soil Pore Architecture","authors":"José V. Gaspareto, Luiz F. Pires","doi":"10.3390/agriengineering6030132","DOIUrl":"https://doi.org/10.3390/agriengineering6030132","url":null,"abstract":"Integrated crop–livestock production (ILP) is an interesting alternative for more sustainable soil use. However, more studies are needed to analyze the soil pore properties under ILP at the micrometer scale. Thus, this study proposes a detailed analysis of the soil pore architecture at the micrometer scale in three dimensions. For this purpose, samples of an Oxisol under ILP subjected to minimum tillage (MT) and no tillage (NT) with ryegrass as the cover crop (C) and silage (S) were studied. The micromorphological properties of the soil were analyzed via X-ray microtomography. The MT(C) system showed the highest values of porosity (c. 20.4%), connectivity (c. 32.8 × 103), volume (c. 26%), and the number of pores (c. 32%) in a rod-like shape. However, the MT(S), NT(C), and NT(S) systems showed greater tortuosity (c. 2.2, c. 2.0, and c. 2.1) and lower pore connectivity (c. 8.3 × 103, c. 6.9 × 103, and c. 6.2 × 103), especially in S use. Ellipsoidal and rod-shaped pores predominated over spheroidal and disc-shaped pores in all treatments. The results of this study show that the use of ryegrass as a cover crop improves the soil physical properties, especially in MT. For S use, the type of soil management (MT or NT) did not show any differences.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829005","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":"Influence of Post-Harvest Processing and Drying Techniques on Physicochemical Properties of Thai Arabica Coffee","authors":"Sai Aung Moon, S. Wongsakul, Hiroaki Kitazawa, R. Saengrayap","doi":"10.3390/agriengineering6030129","DOIUrl":"https://doi.org/10.3390/agriengineering6030129","url":null,"abstract":"Coffee post-processing drying eliminates moisture content, reduces fungal and microbe growth, and develops unique aroma and flavor compounds. Thai coffee producers use controlled-environment drying (CED) techniques to improve the quality and cupping scores of the coffee. This research investigated how different drying methods, including sun drying (SD), controlled-environment drying at 20–30 °C, 50–55% RH, and fast drying (FD) at 30–45 °C influenced the physicochemical characteristics of coffee undergoing dry (DP), washed (WP), and honey (HP) processing. Results showed that true density, moisture content, water activity, color, caffeine, trigonelline, chlorogenic acid, caffeic acid, sucrose, and fructose in green coffee beans were significantly affected (p < 0.05) by both drying technique and post-harvest processing. Drying techniques and processing directly impacted the characteristics of green (GCB) and roasted coffee beans (RCB). Findings suggested a correlation between CED, SD, and FD based on the physicochemical and biochemical properties and sugar contents of both green and roasted coffee beans.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832544","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":"Evaluation of the Efficiency of Machine Learning Algorithms for Identification of Cattle Behavior Using Accelerometer and Gyroscope Data","authors":"Tsvetelina Mladenova, Irena Valova, B. Evstatiev, N. Valov, Ivan Varlyakov, Tsvetan Markov, S. Stoycheva, Lora Mondeshka, Nikolay Markov","doi":"10.3390/agriengineering6030128","DOIUrl":"https://doi.org/10.3390/agriengineering6030128","url":null,"abstract":"Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: “standing and eating”, “standing and ruminating”, and “laying and ruminating”. Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832853","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":"Modification and Performance Evaluation of a Biomass Pelleting Machine","authors":"S. O. Jekayinfa, Folorunso Adegboyega Ola, F. Akande, Mutairu Abiola Adesokan, Ibrahim Akinola Abdulsalam","doi":"10.3390/agriengineering6030130","DOIUrl":"https://doi.org/10.3390/agriengineering6030130","url":null,"abstract":"The use of biomass as a source of energy has been identified to be energy intensive, involving high handling costs. However, pelletization reduces the bulk density of biomass, thereby reducing the handling costs and enhancing ease of use. This study modified and evaluated an existing hand-operated fish feed pelleting machine. The parts of the machine that were redesigned were the hopper and the power transmission unit. Corncob was used to evaluate the modified machine using the die hole diameter (5, 6 and 7 mm) and the binder quantity (0, 2.5 and 5 wt%) as factors. The average results obtained for machine efficiency, throughput, pellet length and bulk density were 58.83%, 4.24 kg/h, 15.51 mm and 0.160 g/cm3, respectively. The die hole diameter had a significant effect on the pellet length only. The binder quantity had a significant effect on machine efficiency, throughput and pellet length. Machine efficiency and throughput decreased as the quantity of binder increased, and the pellet length increased with the increasing quantity of binder.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832242","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":"Influence of Temperature and LED Light Spectra on Flavonoid Contents in Poa pratensis","authors":"Andreas Schweiger, Heinz Bernhardt","doi":"10.3390/agriengineering6030127","DOIUrl":"https://doi.org/10.3390/agriengineering6030127","url":null,"abstract":"Light and temperature are the driving forces in plant development and growth. Specific photoreceptors provide the ability to sense and interpret light and temperature to regulate growth. Under the limited light conditions in most sports stadiums, natural grasses suffer from light deficiency. Artificial light provided by light-emitting diodes (LEDs) is used to increase their growth and adjust their development. Flavonoids like flavonols and anthocyanins are influenced by light conditions and temperature. Increased blue light can elevate the content of these secondary metabolites. Remote measurements of internal parameters using non-destructive methods provided information on their content under different temperature conditions for quality monitoring. This experiment tested flavonoid contents in Kentucky bluegrass (Poa pratensis) for different blue-to-red light ratios (0.6 and 0.4) and three temperature courses (constant temperature of 4 °C, constant temperature of 12 °C, and temperature switching among 12–8–4–8–12 °C). The results show elevated levels of flavonoids under blue-dominant artificial light as well as increased content under low-temperature (4 °C) conditions. The lack of flavonoids at elevated temperatures (12 °C), especially under red-dominant light, suggests an increased requirement for artificial blue light at increased temperatures. Non-destructive flavonoid determination was suitable for this experiment and can therefore be used for practical sports turf quality monitoring.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"49 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654246","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":"Interplay of Fogponics and Artificial Intelligence for Potential Application in Controlled Space Farming","authors":"Newton John O. Suganob, Carey Louise B. Arroyo, Ronnie S. Concepcion","doi":"10.3390/agriengineering6030126","DOIUrl":"https://doi.org/10.3390/agriengineering6030126","url":null,"abstract":"Most studies in astrobotany employ soil as the primary crop-growing medium, which is being researched and innovated. However, utilizing soil for planting in microgravity conditions may be impractical due to its weight, the issue of particles suspended in microgravity, and its propensity to harbor pathogenic microorganisms that pose health risks. Hence, soilless irrigation and fertigation systems such as fogponics possess a high potential for space farming. Fogponics is a promising variation of aeroponics, which involves the delivery of nutrient-rich water as a fine fog to plant roots. However, evaluating the strengths and weaknesses of fogponics compared to other soilless cultivation methods is essential. Additionally, optimizing fogponics systems for effective crop cultivation in microgravity environments is crucial. This study investigated the interaction of fogponics and artificial intelligence for crop cultivation in microgravity environments, aiming to replace soil-based methods, filling a significant research gap as the first comprehensive examination of this interplay in the literature. A comparative assessment of soilless fertigation and irrigation techniques to identify strengths and weaknesses was conducted, providing an overview through a literature review. This highlights key concepts, methodologies, and findings, emphasizing fogponics’ relevance in space exploration and identifying gaps in current understanding. Insights suggest that developing adaptive fogponics systems for microgravity faces challenges due to uncharacterized fog behavior and optimization complexities without gravity. Fogponics shows promise for sustainable space agriculture, yet it lags in technological integration compared with hydroponics and aeroponics. Future research should focus on microgravity fog behavior analysis, the development of an effective and optimized space mission-compatible fogponics system, and system improvements such as an electronic nose for an adaptive system fog chemical composition. This study recommends integrating advanced technologies like AI-driven closed-loop systems to advance fogponics applications in space farming.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"94 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658028","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":"Optimized Walking Route Method for Precision Coffee Farming","authors":"R. O. Faria, Fábio Moreira da Silva, G. Ferraz, Mirian de Lourdes Oliveira e Silva, Miguel Angel Diaz Herrera, Daniel V. Soares, A. C. Marques Filho","doi":"10.3390/agriengineering6030125","DOIUrl":"https://doi.org/10.3390/agriengineering6030125","url":null,"abstract":"Coffee production has become increasingly technified in order to optimize the use of inputs and the sustainable use of natural resources. In this context, one way that farmers are investing in their coffee plantations is in the use of precision agriculture techniques, termed precision coffee farming. Over the last few years, research has been conducted to facilitate the application of this technology, and sampling grids with two points per hectare have been recommended by several studies. These georeferenced demarcations in a plot are generally shaped as equidistant squares or rectangles, and the sampling points are located at the centers of these areas. Coffee farmers typically plant their crops following the level line, which greatly hinders the navigation of equidistant points within the field. Thus, the objective of this study was to develop an optimized walking route method to reduce the distance for sampling soil, leaf, and yield attributes. The experimental plots were established in 2000 at Samambaia Farm, located in Santo Antônio do Amparo, Minas Gerais, Brazil, with coffee the cultivar Acaia IAC 479-19, totaling 56.65 ha. The 111 sampling points were distributed in the land following the new method proposed in this study, and, after walking simulations using Farm Works Mapping Software, the new method was compared with the conventional method using the mean displacement between points. The new optimized walking routes method reduced the mean distance traveled to sample the points by 50.1%.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"31 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659581","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":"ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology","authors":"Ahmad Amirivojdan, A. Nasiri, Shengyu Zhou, Yang Zhao, H. Gan","doi":"10.3390/agriengineering6030124","DOIUrl":"https://doi.org/10.3390/agriengineering6030124","url":null,"abstract":"This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"104 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666489","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}