https://www.agroengineering.org/jae/issue/feedJournal of Agricultural Engineering2024-03-28T09:55:18+00:00Emanuela Fusinatoemanuela.fusinato@pagepress.orgOpen Journal Systems<p>The <strong>Journal of Agricultural Engineering (JAE)</strong> covers a complete and interdisciplinary range of research topics in engineering for agriculture food, forestry and biosystems. The journal publishes papers of both theoretical and applied nature, with a special focus on experimental research, new design criteria and innovative approaches, relating to all fields of agricultural engineering.<br /><strong>JAE</strong> is the official journal of the <a href="http://www.aiia.it" target="_blank" rel="noopener"><strong>Italian Society of Agricultural Engineering</strong></a>.</p>https://www.agroengineering.org/jae/article/view/1557Structural strength analysis of a rotary drum mower during harvesting2024-02-16T14:28:52+00:00H. Kursat Celikhkcelik@akdeniz.edu.trIbrahim Akinciiakinci@akdeniz.edu.trNuri Caglayannuricaglayan@akdeniz.edu.trAllan E.W. Renniea.rennie@lancaster.ac.uk<p>A rotary drum mower (RDM) is a tractor-mounted mechanism used for harvesting green fodder crops. It faces dynamic forces from rough field surfaces and cutting resistance, posing design challenges and potential failures. This study aims to present a well-designed procedure for analyzing the structural strength of an RDM during harvesting, employing both experimental and engineering simulation methods. A specific harvesting scenario was created to simulate realistic load conditions. Experimental testing and advanced computer-aided engineering (CAE) simulations were conducted. Tractor power take-off torque measurements during harvesting revealed values of 231.07 Nm, 264.44 Nm, and 269.39 Nm at speeds of 8.56 km h<em>-1</em>, 12.6 km h<sup>-1</sup>, and 16.23 km h<sup>-1</sup>, respectively. Finite element analysis (FEA) was conducted to determine stress levels in the RDM components (RDM165-A- 004, RDM165-B-003, and RDM165-B-004). The FEA stress results ranged from 5.070 MPa to 20.600 MPa, 13.800 MPa to 28.600 MPa, and 5.400 MPa to 27.550 MPa, respectively. Experimental testing yielded stress results ranging from 2.127 MPa to 18.600 MPa, 14.618 MPa to 33.229 MPa, and 8.838 MPa to 31.248 MPa, respectively. The comparison between experimental and FEA results showed a reasonable correlation. FEA visual outputs provided insights into the maximum equivalent stress and deformation distributions on the RDM, with no indications of failure in the machine’s structure observed in either the experimental or numerical analyses. In conclusion, this study demonstrates that the machine analyzed operates safely under harvesting conditions. Moreover, the combination of experimental and advanced CAE methodologies presented in this research offers a valuable approach for future investigations into the complex stress and deformation evaluations of rotary drum mowers.</p>2024-02-16T00:00:00+00:00Copyright (c) 2024 the Author(s)https://www.agroengineering.org/jae/article/view/1559An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics2024-02-16T14:28:50+00:00Marko Milan Kostićmarkok@polj.uns.ac.rsNataša Ljubičićmarkok@polj.uns.ac.rsVladimir Aćinmarkok@polj.uns.ac.rsMilan Mirosavljevićmarkok@polj.uns.ac.rsMaša Budjenmarkok@polj.uns.ac.rsMiloš Rajkovićmarkok@polj.uns.ac.rsNebojša Dedovićmarkok@polj.uns.ac.rs<p>The ambition of this study was to justify the possibility of wheat trait prediction using a normalized difference vegetation index (NDVI) from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker’s, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, P<0.01). The Plant-O-Meter’s NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21<sup>st</sup>) and late (June 6<sup>th</sup>) growing seasons. NDVI<sub>GreenSeeker</sub> data improved yield modeling performance by an average of 5.1% when compared to NDVI<sub>Plant-O-Meter</sub>; in terms of plant height prediction, NDVI<sub>GreenSeeker</sub> was 3% more accurate than NDVI<sub>Plant-O-Meter</sub> and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.</p>2024-02-16T00:00:00+00:00Copyright (c) 2024 the Author(s)https://www.agroengineering.org/jae/article/view/1553Kinematic model for mechanical apple blossom thinning2024-01-23T15:29:19+00:00Daniel Veallammers@uni-bonn.deLutz Damerowlammers@uni-bonn.dePeter Schulze Lammerslammers@uni-bonn.deMichael Blankelammers@uni-bonn.de<p>The international apple trade requires apples with diameters of over 70 mm. Left untouched, apple trees tend to produce many apples of small diameter. To increase apple size, the number of blossoms can be reduced in their early growth stage, leaving fewer apples that will grow larger because of access to a greater portion of nutrients. Over the past few decades this has been mainly accomplished through chemical means, but recent demand for sustainable fruit production with fewer chemicals requires means of blossom thinning using, <em>e.g.</em>, mechanical methods, <em>i.e.</em>, a machine with rotors and brushes. The goal of this project was to perform kinematic analysis on such a mechanical thinning machine to model the motion and behavior, both mathematically and graphically, as well as offer recommendations of operating parameters to maximize the machine’s efficiency. The project involved creating and assembling a three-dimensional model of the machine in Pro/ENGINEER, performing kinematic analysis on the model, using the output to produce a mathematical formula, and using that formula to both analyze and predict the operation of the machine. The mathematical model was verified successfully against field test data. It was then used to provide tractor and rotor speeds for a range of desired percentage of blossoms removed. It also accomplished the reverse, predicting the percentage of blossoms removed for a series of chosen tractor and rotor speeds.</p>2024-01-23T00:00:00+00:00Copyright (c) 2024 the Author(s)https://www.agroengineering.org/jae/article/view/1544Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification2023-10-30T11:23:20+00:00Xiong Biwanghc@cqnu.edu.cnHongchun Wangwanghc@cqnu.edu.cn<p>Deep convolutional neural network (DCNN) has recently made significant strides in the classification and recognition of rice leaf disease. The majority of classification models perform disease image recognitions using collocation patterns including pooling layers, convolutional layers, and fully connected layers, followed by repeating this structure to complete depth increase. However, the key information of the lesion area is locally limited. That is to say, in the case of only performing feature extraction according to the above-mentioned model, redundant and low-correlation image feature information with the lesion area will be received, resulting in low accuracy of the model. For improvement of the network structure and accuracy promotion, here we proposed a double-branch DCNN (DBDCNN) model with a convolutional block attention module (CBAM). The results show that the accuracy of the classic models VGG-16, ResNet-50, ResNet50+CBAM, MobileNet-V2, GoogLeNet, EfficientNet-B1 and Inception-V2 is lower than the accuracy of the model in this paper (98.73%). Collectively, the DBDCNN model here we proposed might be a better choice for classification and identification of rice leaf diseases in the future, based on its novel identification strategy for crop disease diagnosis.</p>2023-10-30T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1549Apple recognition and picking sequence planning for harvesting robot in a complex environment2024-03-28T09:55:16+00:00Wei Jijiwei@ujs.edu.cnTong Zhangjiwei@ujs.edu.cnBo Xujiwei@ujs.edu.cnGuozhi Hejiwei@ujs.edu.cn<p>In order to improve the efficiency of robots picking apples in challenging orchard environments, a method for precisely detecting apples and planning the picking sequence is proposed. Firstly, the EfficientFormer network serves as the foundation for YOLOV5, which uses the EF-YOLOV5s network to locate apples in difficult situations. Meanwhile, the soft non-maximum suppression algorithm is adopted to achieve accurate identification of overlapping apples. Secondly, the adjacently identified apples are automatically divided into different picking clusters by the improved density-based spatial clustering of applications with noise. Finally, the order of apple harvest is determined to guide the robot to complete the rapid picking, according to the weight of the Gauss distance weight combined with the significance level. In the experiment, the average precision of this method is 98.84%, which is 4.3% higher than that of YOLOV5s. Meanwhile, the average picking success rate and picking time are 94.8% and 2.86 seconds, respectively. Compared with sequential and random planning, the picking success rate of the proposed method is increased by 6.8% and 13.1%, respectively. The research proves that this method can accurately detect apples in complex environments and improve picking efficiency, which can provide technical support for harvesting robots.</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1547Variable-rate spray system for unmanned aerial applications using lag compensation algorithm and pulse width modulation spray technology2023-10-31T13:16:03+00:00Zhongkuan Wangvincen@scau.edu.cnSheng Wenvincen@scau.edu.cnYubin Lanvincen@scau.edu.cnYue Liuvincen@scau.edu.cnYingying Dongvincen@scau.edu.cn<p>To ensure that a variable-rate spray (VRS) system can perform unmanned aerial spray in accordance with a prescription map at different flight speeds, we examine in this paper such significant factors as the response time of the VRS system and the pressure fluctuation of the nozzle during the variable-rate spraying process. The VRS system uses a lag compensation algorithm (LCA) to counteract the droplet deposition position lag caused by the system response delay. In addition, pulse width modulated solenoid valves are used for controlling the flowrates of the nozzles on the variablerate spray system, and a mathematical model was constructed for the spray rate (L min<sup>-1</sup>) and the relative proportion of time (duty cycle) each solenoid valve is open. The pressure drop and solenoid valve response time at different duty cycles (50~90%) were measured by indoor experiments. Meanwhile, the lag distance (LD), spray accuracy, and droplet deposition characteristics of the VRS system were tested by conducting outdoor experiments at different flight speeds (4m s<sup>-1</sup>, 5m s<sup>-1</sup>, 6m s<sup>-1</sup>). The results show that LCA can effectively reduce the LD. The LD values of the VRS system with LCA ranged from -0.27 to 0.78m with an average value of 0.32m, while without LCA, the LD values increased to 3.5~4.3m with an average value of 3.87m. The overall spray position accuracy was in the range of 91.56~97.32%. Furthermore, the spray coverage and deposition density, determined using water sensitive paper, were used to evaluate the spray application performance taking into account the spray volume applied. The VRS system can provide the most suitable spray volumes for insecticide and fungicide plant protection products. Based on a prescription map, the optimized VRS system can achieve accurate pesticide spraying as well as desirable spray coverage and deposition density.</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1545Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence2024-03-28T09:46:45+00:00Dhanashree Barbolemanedhanashree04@gmail.comParul M. Jadhavmanedhanashree04@gmail.com<p>Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, and costly, and its accuracy depends on the scale of the sample. To overcome these problems, hybrid approaches of computer vision, deep learning (DL), and machine learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally, a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy up to 94.81% and yield prediction accuracy up to 99.58%.</p>2023-10-30T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1550Comparison of two different artificial neural network models for prediction of soil penetration resistance2024-03-28T09:55:05+00:00İlker Ünalonderkabas@hotmail.comÖnder Kabaşonderkabas@hotmail.comSalih Sözeronderkabas@hotmail.com<p>A time-varying, nonlinear soil-plant system contains many unknown elements that can be quantified based on analytical methodologies. Artificial neural networks (ANNs) are a widely used mathematical computing, modeling, and predicting methods that estimate unknown values of variables from known values of others. This paper aims to simulate the relationship between soil moisture, bulk density, porosity ratio, depth, and penetration resistance and to estimate soil penetration resistance with the help of ANNs. For this aim, the generalized regression neural network (GRNN) and radial basis function (RBF) models were developed and compared for the estimation of soil penetration resistance values in MATLAB. A dataset of 153 samples was collected from experimental field. From the 153 data, 102 data (33%) were selected for training and the remaining 51 data (67%) were used for testing. The estimation process implemented 10 replications using randomly selected testing and training data. mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate estimation accuracy on the developed ANN methods. Based on MSE, RMSE, MAE and standard deviation, statistical results showed that the GRNN modeling presented better results than the RBF model in predicting soil penetration resistance success.</p>2023-12-29T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1556The results of experimental research of a rotor seed-metering unit for sowing non-free-flowing seeds2024-02-08T14:28:56+00:00Shinar Ospanovashinarospanova317@gmail.comMubarak Aduovshinarospanova317@gmail.comSultan Kapovshinarospanova317@gmail.comAlexander Orlyanskyshinarospanova317@gmail.comKadirbek Volodyashinarospanova317@gmail.com<p>The production and cultivation of new high-quality seed varieties are linked to the sowing of various crops with diverse physical and mechanical seed properties. Efficient seed-metering unit operation is critical during the technological process of fodder crop cultivation, predominantly when sowing non-free-flowing seeds. The quality of seed sowing and crop yield significantly rely on the design precision of seed-metering devices, technical maintenance and appropriate calibration. A rotary seed metering device was incorporated to ensure that non-friable seeds are uniformly sown, thus maintaining consistent seed supply and consumption at all stages of circulation. The study of the proposed device’s productivity dependence on its operating parameters is justified because these variables affect crucial indicators such as the capacity to achieve and sustain the desired seeding rate over the entire operational duration. The study presents findings from an experimental investigation on sowing non-free-flowing (non-flowing) and finely dispersed seeds using a rotor seed-metering unit. The tests aimed to ascertain the precision and evenness of sowing such crops. It was observed that the speed of rotation of the seed-metering unit’s vane disk is a key factor in the uniformity and supply of sown seeds. The limits of variation in rotor rotation speed and rotor seed-metering unit productivity per second were established to guarantee the desired seeding rate for various crops, including alfalfa, <em>Agropyron</em>, and <em>Bromus inermis</em>.</p>2024-02-08T00:00:00+00:00Copyright (c) 2024 the Author(s)https://www.agroengineering.org/jae/article/view/1548Monitoring and multi-scenario simulation of agricultural land changes using Landsat imageries and future land use simulation model on coastal of Alanya2024-03-28T09:55:18+00:00Melis Inalpulatmelissacan@comu.edu.tr<p>Anthropogenic activities have adverse impacts on productive lands around coastal zones due to rapid developments. Assessment of land use and land cover (LULC) changes provide a better understanding of the process for conservation of such vulnerable ecosystems. Alanya is one of the most popular tourism hotspots on the Mediterranean coast of Turkey, and even though the city faced severe LULC changes after the mid-80s due to tourism-related investments, limited number of studies has been conducted in the area The study aimed to determine short-term and long-term LULC changes and effects of residential development process on agricultural lands using six Landsat imageries acquired between 1984 and 2017, and presented the first attempt of future simulation in the area. Average annual conversions (AAC) (ha) were calculated to assess magnitudes of annual changes in six different periods. AACs were used to calculate area demands for LULC<sub>2030</sub> and LULC<sub>2050</sub>, whereby annual conversions from different periods were multiplied by the number of years between 2017, 2030 and 2050 for each scenario. Finally, optimistic and pessimistic scenarios for agricultural lands are simulated using a future land use simulation model. Accordingly, agricultural lands decreased from 53.9% to 31.4% by 22.5% in 33 years and are predicted to change between 19.50% and 24.63% for 2030, 1.07% and 14.10% for 2050, based on pessimistic and optimistic scenarios, respectively.</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1546Design and experiment of furrow side pick-up soil blade for wheat strip-till planter using the discrete element method2023-10-30T13:00:25+00:00Lei Liuwxl1990@sdut.edu.cnXianliang Wangwxl1990@sdut.edu.cnXiaokang Zhongwxl1990@sdut.edu.cnXiangcai Zhangwxl1990@sdut.edu.cnYuanle Gengwxl1990@sdut.edu.cnHua Zhouwxl1990@sdut.edu.cnTao Chenwxl1990@sdut.edu.cn<p>The strip rotary tillage method effectively reduces the occurrence of straw clogging and creates a favorable seed bed environment. However, the mixture of crushed straw and soil in the seeding area results in inadequate seed-soil contact following compaction by the press wheels. A chisel-type opener furrow side pick-up blade was proposed to improve seed-soil contact by picking up wet soil from the furrow’s side. The discrete element method was used to investigate the impact of earth blade surface parameters on soil dynamics. The key factors of the blade, including forward velocity, endpoint tangent angle, and angle of soil entry, were determined through theoretical analysis. Soil cover thickness and straw ratio in the seed furrow were evaluated using orthogonal rotation regression tests. The results show that the endpoint tangent angle and angle of soil entry have the greatest influence on soil cover thickness, while the angle of soil entry has the greatest influence on the straw ratio. The optimal values for the forward velocity, endpoint tangent angle, and angle of soil entry are 4.86 km/h, 107.17°, and 5.46°, respectively, resulting in a soil cover thickness of 40 mm and a straw ratio of 21.46%. Confirmatory soil bin tests showed similar results, with a soil cover thickness of 40.4 mm and a straw ratio of 18.03%. These results provide a viable solution for improving seed-soil contact after strip rotary tillage planter seeding.</p>2023-10-30T00:00:00+00:00Copyright (c) 2023 the Author(s)https://www.agroengineering.org/jae/article/view/1552Studies of tractor maintenance and replacement strategies of Wonji Shoa Factory, Ethiopia2024-03-28T09:46:41+00:00Kishor P. Kolhekishorkolhe05@gmail.comDemelash G. Lemidemelashgindo4@gmail.comSiraj K. Bussesrjkedir@gmail.com<p>This study mainly focuses on tractor maintenance and replacement strategies to assess the impact of various parameters on the economic life of tractors in order to improve the value of a profitable management choice on selected tractor samples. Considering the preventive replacement policy, the total annual costs were estimated taking into account the repair and depreciation costs. At a 95% level of confidence for each approach, the statistical analysis program “IBM SPSS Statistics 26” was used. An empirical relation based on multiple regression analysis has been generated to predict the economic operational life of a tractor using per-unit repair cost and annual usage (hours). From the analysis, John Dear 333, SAME 130, New Holland 80, and Massy Ferguson 150 are not supposed to be used economically in the field after the fifth, seventh, sixth, and eighth years respectively at Wonji Shoa Sugar Factory due to increasing maintenance cost in present condition.</p>2024-01-23T00:00:00+00:00Copyright (c) 2024 the Author(s)