AgriEngineering最新文献

{"title":"Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height","authors":"Uriel Cholula, Manuel A. Andrade, Juan K. Q. Solomon","doi":"10.3390/agriengineering6030123","DOIUrl":"https://doi.org/10.3390/agriengineering6030123","url":null,"abstract":"In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"115 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667728","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":"Implementing Internet of Things for Real-Time Monitoring and Regulation of Off-Season Grafting and Post-Harvest Storage in Citrus Cultivation: A Case Study from the Hilly Regions of Nepal","authors":"R. Lamsal, U. K. Acharya, P. Karthikeyan, Pablo Otero, Alfonso Ariza","doi":"10.3390/agriengineering6030122","DOIUrl":"https://doi.org/10.3390/agriengineering6030122","url":null,"abstract":"Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"111 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668287","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":"Investigating the Impact of Speed and Tire Pressure of a Wheel Tractor on Soil Properties: A Case Study in Northeastern Uzbekistan","authors":"Аdilbek Akhmetov, Sherzodbek Akhmedov, J. Ishchanov","doi":"10.3390/agriengineering6030121","DOIUrl":"https://doi.org/10.3390/agriengineering6030121","url":null,"abstract":"In agriculture, machines engaged in various agrotechnical activities and operations have different impacts on the soil. The effect of mechanization is primarily reflected in two indicators: soil density and hardness. At the same time, considering the direct dependence of tractive resistance on soil hardness in processing machines and sprayers, we studied subsequent changes in the soil in the path of wheels affected by the soil after the passage of four-wheeled and three-wheeled tractors. We also examined various atmospheric pressures in the tractor’s tires and the impact of different types of tires on soil compaction and traction. The studies showed that to reduce the compression impact on the soil of four-wheeled tractor working systems during certain technical operations, it is necessary to choose the maximum permissible travel speed and the minimum air pressure in the tires specified in the technical conditions. This approach helps to decrease soil compaction and maintain its structure. Additionally, it was found that three-wheeled tractors exert less pressure on the soil compared to four-wheeled ones, which should also be considered when selecting equipment for different agrotechnical tasks. Optimizing tire pressure and tractor speed is crucial for minimizing negative soil impact and enhancing the efficiency of agricultural operations.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"67 s307","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141683302","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":"Emerging Agricultural Engineering Sciences, Technologies, and Applications","authors":"Muhammad Sultan","doi":"10.3390/agriengineering6030120","DOIUrl":"https://doi.org/10.3390/agriengineering6030120","url":null,"abstract":"The closing Editorial of this comprehensive special collection presents the journey from this project’s inception to the publication of around five dozen outstanding studies that have been a testament to the dedication, innovation, and collective wisdom of the global agricultural engineering community [...]","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681085","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":"Growth Monitoring of Greenhouse Tomatoes Based on Context Recognition","authors":"Fisilmi Azizah Rahman, Miho Takanayagi, Taiga Eguchi, Wen Liang Yeoh, Nobuhiko Yamaguchi, Hiroshi Okumura, Munehiro Tanaka, Shigeki Inaba, Osamu Fukuda","doi":"10.3390/agriengineering6030119","DOIUrl":"https://doi.org/10.3390/agriengineering6030119","url":null,"abstract":"To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"40 161","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696520","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":"Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement","authors":"Oumayma Jouini, Mohamed Ould-Elhassen Aoueileyine, K. Sethom, Anis Yazidi","doi":"10.3390/agriengineering6030117","DOIUrl":"https://doi.org/10.3390/agriengineering6030117","url":null,"abstract":"Improving agricultural productivity is essential due to rapid population growth, making early detection of crop diseases crucial. Although deep learning shows promise in smart agriculture, practical applications for identifying wheat diseases in complex backgrounds are limited. In this paper, we propose CropNet, a hybrid method that utilizes Red, Green, and Blue (RGB) imaging and a transfer learning approach combined with shallow convolutional neural networks (CNN) for further feature refinement. To develop our customized model, we conducted an extensive search for the optimal deep learning architecture. Our approach involves freezing the pre-trained model for feature extraction and adding a custom trainable CNN layer. Unlike traditional transfer learning, which typically uses trainable dense layers, our method integrates a trainable CNN, deepening the architecture. We argue that pre-trained features in transfer learning are better suited for a custom shallow CNN followed by a fully connected layer, rather than being fed directly into fully connected layers. We tested various architectures for pre-trained models including EfficientNetB0 and B2, DenseNet, ResNet50, MobileNetV2, MobileNetV3-Small, and Inceptionv3. Our approach combines the strengths of pre-trained models with the flexibility of custom architecture design, offering efficiency, effective feature extraction, customization options, reduced overfitting, and differential learning rates. It distinguishes itself from classical transfer learning techniques, which typically fine-tune the entire pre-trained network. Our aim is to provide a lightweight model suitable for resource-constrained environments, capable of delivering outstanding results. CropNet achieved 99.80% accuracy in wheat disease detection with reduced training time and computational cost. This efficient performance makes CropNet promising for practical implementation in resource-constrained agricultural settings, benefiting farmers and enhancing production.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"35 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702635","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":"Sorption Drying of Wheat Seeds Using Kieserite as a Solid Desiccant","authors":"E. Fomenko, N. Anshits, Vasily F. Shabanov, A. Anshits","doi":"10.3390/agriengineering6030118","DOIUrl":"https://doi.org/10.3390/agriengineering6030118","url":null,"abstract":"The moisture content (MC) of wheat seeds must be reduced before storage using appropriate dehydration processes. Desiccant drying is a promising alternative to conventional drying methods because it improves seed quality while providing overall energy efficiency. This study explores the sorption drying of wheat seeds using granulated kieserite MgSO4·H2O as a solid desiccant, which has a high water capacity and is regenerated at low temperatures <100 °C. Desiccant characterization was conducted using SEM-EDS, XRD, DSC-TG, and particle size analysis. Wheat seeds mixed directly with kieserite in various mass ratios were dried under uniform stirring and controlled temperature conditions. A 240-minute drying time was required to reduce the initial MC of wheat from 21.5% to 15.1% at a desiccant-to-grain ratio of 1:1. After 360 min, a final MC of 14.4% was achieved. The germination energy and seed capacity after sorption drying were 91 ± 1% and 97 ± 2%, respectively. Due to the available water capacity of kieserite, several batches of seeds can be dried without intermediate desiccant regeneration. This study is useful for developing low-cost, non-thermal, and sustainable drying technology for various agricultural products.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"194 2‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708239","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":"Numerical Investigation on Flowability of Pulverized Biomass Using the Swelling Bed Model","authors":"M. Przywara, Regina Przywara, Wojciech Zapała","doi":"10.3390/agriengineering6020078","DOIUrl":"https://doi.org/10.3390/agriengineering6020078","url":null,"abstract":"Numerical investigations on the flowability of pulverized biomass are crucial for agriculture, aiding in optimizing biomass use, crop residue management, soil health improvement, and environmental impact mitigation. Rising interest in biomass and conversion processes necessitates deeper property understanding and technological process optimization. Moisture content is a key parameter influencing biomass quality. In this paper, computer simulations of shear tests depending on the moisture content using the discrete element method were carried out and compared with experimental results. An experimental study and modeling for Jenike’s direct shearing apparatus was carried out. A swelling bed model was proposed to account for the effect of moisture. The swelling bed model assumed an increase in biomass grain vorticity proportional to the moisture content. The model was solved using the discrete element method (DEM). The model considers the effect of moisture on the values of Young’s and Kirchoff’s moduli for biomass grains. The model assumed that moisture is not present in surface form, the total amount of moisture is absorbed into the interior of the material grains, and the volume of a single grain increases linearly with an increase in the volume of the absorbed moisture. The tested materials were pulverized sunflower husks, apple pomace, distiller’s dried grains with solubles (DDGS), meat and bone meal (MBM), and sawdust. Samples with moisture contents of 0%, 10%, 20%, and 30% were tested. The best agreement of the model with the experimental data was observed for the most absorbent materials in which moisture was not present in surface form, such as apple pomace, DDGS, and sawdust. Research data are important for the proper design of biomass storage, transportation equipment, and utilization as feedstock for bioenergy production or soil enrichment.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"67 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971931","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":"A Simple Method for Estimating Stomatal Aperture from Temperature Measurements on Intact Leaves and Wet and Dry Artificial Reference Leaves","authors":"Yoshiaki Kitaya, Noboru Ikeda, Ryosuke Endo, Toshio Shibuya","doi":"10.3390/agriengineering6020077","DOIUrl":"https://doi.org/10.3390/agriengineering6020077","url":null,"abstract":"Environmental control in greenhouse horticulture is essential for providing optimal conditions for plant growth and achieving greater productivity and quality. To develop appropriate environmental management practices for greenhouse horticulture through sensing technologies for monitoring the environmental stress responses of plants in real time, we evaluated the relative value of the stomatal opening to develop a technology that continuously monitors stomatal aperture to determine the moisture status of plants. When plants suffer from water stress, the stomatal conductance of leaves decreases, and transpiration and photosynthesis are suppressed. Therefore, monitoring stomatal behavior is important for controlling plant growth. In this study, a method for simply monitoring stomatal conductance was developed based on the heat balance method. The stomatal opening index (SOI) was derived from heat balance equations on intact tomato leaves, wet reference leaves, and dry reference leaves by measuring their temperatures in a growth chamber and a greenhouse. The SOI can be approximated as the ratio of the conductance of the intact leaf to the conductance of the wet reference leaf, which varies from 0 to 1. Leaf temperatures were measured with infrared thermometry. The theoretically and experimentally established SOI was verified with tomato plants grown hydroponically in a greenhouse. The SOI derived by this method was consistent with the leaf conductance measured via the porometer method, which is a standard method for evaluating actual leaf conductance that mainly consists of stomatal conductance. In conclusion, the SOI for the continuous monitoring of stomatal behavior will be useful not only for studies on interactions between plants and the environment but also for environmental management, such as watering at plant production sites.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"41 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981453","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 Radio Frequency Identification Power and Unmanned Aerial Vehicle Altitude in Plant Inventory Applications","authors":"Van Patiluna, J. Maja, James Robbins","doi":"10.3390/agriengineering6020076","DOIUrl":"https://doi.org/10.3390/agriengineering6020076","url":null,"abstract":"In the business of growing and selling ornamental plants, it is important to keep track of plants from nursery to distribution. Radio Frequency Identification (RFID) technology provides an easier tracking method for inventories of plants by attaching tags with unique identifiers. Due to the vast area of most nurseries, there is a need to have an efficient method of scanning RFID tags. This paper investigates the use of drones and RFID, specifically, the effects of RFID reader power and flight altitude on tag counts. The experimental setup evaluated three RFID reader power levels (15 dBm, 20 dBm, and 27 dBm), three flight altitudes (3 m, 5 m, and 7 m), the number of passes (one or two), and two plant types (‘Green Giant’ arborvitae and ‘Sky Pencil’ holly). For RFID tags, four types were used (L5, L6, L8, and L9), with two antenna types (dog-bone and square-wave) and two attachment types (loop-lock and stake). For each power level, the UAV was flown to three different altitudes of 3 m, 5 m, and 7 m above the ground. At each altitude, two scan passes were performed at a constant speed of approximately 1.5 m/s. Each plot of plants (two in total) was randomly tagged with a total of 40 RFID tags per plot. Field data were collected from September to December 2023 (on a total of eight dates). The data showed that a power level of 15 dBm and an altitude of 3 m yielded a tag count of 53%, while counts of 34% and 16% were achieved at 5 m and 7 m, respectively. At 20 dBm and an altitude of 3 m, the count accuracy across all tag types and both plants was 90%. When the altitude was increased to 5 m and 7 m, tag-count accuracy dropped to 75% and 33%, respectively. The highest count accuracy was observed at 27 dBm and an altitude of 3 m, with a reading accuracy of 98%. Tag types L6 and L9 performed better at any power level and altitude, while L5 and L8 performed well at a higher power level and lower altitude. In this experiment, canopy properties (size and shape) had no effect on the number of tags read. This study aimed to evaluate the RFID power and UAV altitude achieving the highest accuracy in scanning the RFID tags. Furthermore, it also assessed the effects of plant growth on the scanning efficiency and accuracy of the system.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140993112","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}