AGRIS - 

2023

Amankulova, Khilola | Farmonov, Nizom | Mucsi, László

Accurate estimates and predictions of sunflower crop yields at the pixel and field level are critically important for farmers, service dealers, and policymakers. Several models based on remote sensing data have been developed in yield assessment, but their robustness—especially in small field scale areas—needs to be examined. Here we aim to develop a robust methodology for estimation/prediction of sunflower yield at pilot field scale using Sentinel-2 remote sensing satellite imagery. We conducted the study in Mezőhegyes, south-eastern Hungary. The Random Forest Regression (RFR), a machine learning technique was used in this research to translate the Sentinel-2 spectral bands to sunflower yield based on crop yield data provided by a combine harvester equipped with a yield-monitoring system. Sentinel-2 images obtained from April to September were used to find the best image for prediction. The satellite image acquired on June 28 was found best and considered further for prediction sunflower yield. A developed training model was tested and validated in 10 different parcels to evaluate the performance of the prediction. We examined the results of the prediction model (predicted) against the actual yield data (observed) collected by a combine harvester. The results demonstrated that using 10 spectral bands from Sentinel-2 imagery the best time to predict sunflower yields was between 85 and 105 d into the growing season during the flowering stage. This model achieved high accuracy with low normalized root means square error (RMSE) ranging from 121.9 to 284.5 kg/ha for different test fields. Our results are promising because they prove the possibility of predicting sunflower grain yield at the pixel or field level, 3–4 months before the harvest, which is crucial for planning food policy.

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National Agricultural Library - United States of America

2023

Prisyazhnaya, I.M. | Prisyazhnaya, S.P.

A new technology for obtaining high-quality conditioned soybean seeds by a separate fraction in two-phase threshing combines during harvesting is suggested. This fraction can be used for sowing without subsequent post-harvest conditioning. Technological and design solutions were developed that allow the threshed grain to be divided into seed and commercial fractions. With two-phase threshing by the first thrashing drum, the most mature biologically full-fledged soybean seeds are ground out in a sparing regimen, separately sorted in the first half of the sieve boot of the combine and separately collected in the first grain auger, then in a two-section tanker. Dividing the undercylinder pan into two parts and preventing mixing of the separated grain, ground by the first and finally ground by the second thrashing drums under hard modes, more than 60% of the high-quality seeds of the first fraction derived by threshing and separating from the first thrashing drum enter the combine tanker separately. The seed fraction with a high weight of 1000 seeds has purity at the level of the first class requirements with high sowing qualities, lower amount of crushing and micro damage and increased performance. The commercial fraction obtained from the second thrashing drum, straw rack, and finish thresher prevents grain losses. | Предложена новая технология получения качественных кондиционных семян сои отдельной фракцией в комбайнах двухфазного обмолота в процессе уборки урожая. Эта фракция без последующей послеуборочной подработки может быть использована на посеве. Разработаны технологические и конструкторские решения, позволяющие разделять обмолоченное зерно по качеству на семенную и товарную фракции. При двухфазном обмолоте первым молотильным барабаном на щадящем режиме вымолачиваются наиболее вызревшие биологически полноценные семена сои, отдельно сортируются на первой половине решетного стана комбайна и раздельно собираются в первом зерновом шнеке, далее в двухсекционном бункере. Разделяя скатную доску на две части и предотвращая смешивание просепарированного зерна, вымолоченного первым и домолоченного вторым молотильными барабанами на жестких режимах, раздельно поступают в бункер комбайна свыше 60% качественных семян первой фракции, выделяемых при обмолоте и сепарации от первого молотильного барабана. Семенная фракция с высокой массой 1000 семян обладает чистотой на уровне требований первого класса с очень хорошими посевными качествами, низкой величиной дробления и микроповреждения, повышенной продуктивностью. Товарная фракция, получаемая от второго молотильного барабана, соломотряса и домолачивающего устройства предотвращает потери зерна.

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Central Scientific Agricultural Library - Russian Federation

2023

Ostrikov, V.V. | Zhernovnikov, D.N. | Zabrodskaya, A.V. | Vyazinkin, V.S. | Safonov, V.V.

Works on closing-down the internal cavities of internal combustion engines, especially cylinder liners are carried out before placing combine harvesters for off-season storage. The equipment manufacturers recommend using special closing-down technologies and specialized storage oils. These corrosion control measures are almost not used in real operation because of the high implementation costs. A low-cost resource-saving technology for protecting combine harvester engines from corrosion-mechanical wear and a storage oil formulation based on the used engine oil are being developed at the All-Russian Research Institute of Machinery and Petroleum Products Use in Agriculture. A method for spent oil decontaminating has been developed. Studies have been carried out to determine the rational protective oil formulation. The polymerized rapeseed oil and gun grease were tested as additives that increase anti-wear and anti-corrosion properties. The optimal oil polymerization time has been established to be at least 20 h, which makes it possible to increase the viscosity of the oil and increase the parameters of the protective film being formed. The AWC gun grease is considered as a corrosion inhibitor included in the protective oil composition. A small amount (3 L) of purified motor oil M-10G2 is taken for mixing AWC with base oil; the mixture is heated to 100 deg. C and mixed for 20 min. The resulting enriching mixture of purified oil with dissolved polymerized rapeseed oil and AWC grease was added to the main matter of purified oil at a concentration of 3%, 6%, 9%. During the studies dependences of the change in viscosity were obtained. It has been determined that 6% is a rational concentration for enriching mixture adding. An assessment of a possibility of precipitation of oil components during their storage showed acceptable values for the thickness of the sediment layer that not exceeding 1 mm. As a first approximation, a new oil formulation has been developed, which is easily accessible for preparing in the conditions of agricultural enterprises of the agro-industrial sector. | Перед постановкой зерноуборочных комбайнов на межсезонное хранение проводят работы по консервации внутренних полостей двигателей внутреннего сгорания, особенно гильз цилиндров. Изготовители техники рекомендуют использовать специальные технологии консервации и специализированные консервационные масла. В условиях реальной эксплуатации эти меры защиты от коррозии почти не применяются из-за высоких затрат на их реализацию. Во Всероссийском НИИ использования техники и нефтепродуктов в сельском хозяйстве разрабатываются малозатратная ресурсосберегающая технология защиты двигателей комбайнов от коррозионно-механического износа и состав консервационного масла на базе отработавшего свой срок моторного масла. Разработан способ очистки отработанного масла от загрязнений. Проведены исследования по определению рационального состава масла. Полимеризованное рапсовое масла и пушечная смазка испытывались в качестве добавок, повышающих противоизносные и антикоррозионные свойства. Установлено оптимальное время полимеризации масла (не менее 20 ч), позволяющее повысить его вязкость и увеличить параметры формируемой защитной плёнки. В качестве ингибитора коррозии в составе консервационного масла рассмотрена пушечная смазка ПВК. Для смешивания ПВК с базовым маслом отбирается незначительное (3 л) количество очищенного моторного масла М-10Г2, производится нагрев смеси до 100 град. С и перемешивание в течение 20 минут. Полученная обогащающая смесь очищенного масла с растворённым полимеризованным рапсовым маслом и смазкой ПВК вносилась в основной объём очищенного масла в концентрации 3%, 6%, 9%. В ходе исследований получены зависимости изменения вязкости. Определено, что рациональная концентрация внесения обогащающей смеси составляет 6%. Оценка возможности осаждения компонентов масел в процессе их хранения показала допустимые значения толщины слоя осадка, не превышающие 1 мм. В первом приближении разработан новый состав масла, легко доступный для получения в условиях сельхозпредприятий АПК.

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Central Scientific Agricultural Library - Russian Federation

2023

Erokhin, G.N. | Konovskij, V.V. | Pershin, I.A.

A model for estimating the fuel consumption of a combine harvester in the process of harvesting soybeans is considered. The model is based on predicting the fuel consumption during the periods of operating time of the combine, during which the engine is running, the time spent on the main work, unloading soybean grains, moving to the place of unloading and idle moving. A general equation for the specific fuel consumption of a combine harvester in these periods of time is obtained. The model also considers the impact on fuel consumption of the combine thresher loading in terms of throughput capability. The model allows predicting the fuel consumption of each harvester and in total for an agricultural enterprise. The model approbation was carried out in a specific agricultural enterprise located in the Tambov region. Justified by the model, the required amount of diesel fuel for soybean harvesting was 4901 L. In fact, 5488 L were spent. The relative error in forecasting the total fuel consumption for harvesting soybeans for the whole enterprise was 10.7%. The dependences of the specific fuel consumption on the operating speed are obtained using the example of the ACROS 550 and KZS-1218A-1 combine. For both brands of combines, a decrease in the specific fuel consumption with an increase in operating speed was revealed. The most significant decrease is observed in the speed range of 2…6 km/h. With an increase in the operating speed of 2 km/h to 6 km/h, the specific fuel consumption of combines decreases on average by 1.9 times. The fuel consumption decreases as the yield grows and increases as the straw content in the harvested crop rises. The significance of the influence of these factors and the need to take them into account in the analysis and forecast of fuel consumption for soybean harvesting are confirmed. | Рассматривается модель оценки расхода топлива зерноуборочным комбайном в процессе уборке сои. Модель базируется на прогнозировании расхода топлива в периоды эксплуатационного времени работы комбайна, в течение которых работает двигатель (затраты времени на основную работу, выгрузку зерна сои, переезды к месту выгрузки и холостые переезды). Получено общее уравнение удельного расхода топлива комбайном в этих периодах времени. В модели учтено также влияние на расход топлива загрузки молотилки комбайна по пропускной способности. Модель позволяет прогнозировать расход топлива каждым комбайном и в целом по сельхозпредприятию. Апробация модели проведена в конкретном сельхозпредприятии Тамбовской области. Обоснованное по модели необходимое количество дизельного топлива для проведения уборки сои составило 4901 л. Фактически было израсходовано 5488 л. Относительная ошибка прогнозирования общего расхода топлива для уборки сои в целом по предприятию составила 10,7%. Получены зависимости удельного расхода топлива от рабочей скорости на примере комбайнов ACROS 550 и КЗС-1218А-1. Для обоих марок комбайнов выявлено снижение удельного расхода топлива с ростом рабочей скорости. Наиболее значимое снижение наблюдается в интервале скорости 2…6 км/ч. При увеличении рабочей скорости от 2 км/ч до 6 км/ч удельный расход топлива у комбайнов в среднем уменьшается в 1,9 раза. Расхода топлива сокращается по мере роста урожайности и увеличивается по мере повышения соломистости убираемой культуры. Подтверждена значимость влияния указанных факторов и необходимость их учета при анализе и прогнозировании расхода топлива на уборке сои.

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Central Scientific Agricultural Library - Russian Federation

2023

Skorlyakov, V.I. | Revenko, V.Yu. | Nazarov, A.N.

The purpose of the study is to develop methods for assessing the relative length of the combine harvester passage, and the field area which soil is impacted with the wheels of the combine harvester in excess of the regulated allowable limit. When developing a method for assessing soil compaction by driving wheels of a combine in excess of permissible limits, we proceeded from the following provisions: the weight characteristics of a combine with an empty and filled hopper correspond to individual maximum pressure indicators, which can be determined by weighting and subsequent calculations. During the operation of the combine, when grain enters the bunker, each value of the route length corresponds to a certain value of the maximum pressure of the wheel on the soil. Therefore, setting the value of the maximum pressure allowed for the soil, regulated by GOST (State Standard) R 58655, and solving the equation, you can get the value of the passage length, after which the maximum the wheel pressure on the soil will exceed the value allowed by the standard. Finding this point will make it possible to delimit the section of the length of the passage of the combine with an allowable pressure on the soil from the section with excess pressure and to estimate the overcompacted area of the field in excess of the allowable value, which is ensured by performing a series of successive actions. The presented method of calculation makes it possible to determine the relative area of the field, compacted by a combine harvester in excess of the permissible limits of maximum pressure on the soil. It has been established by calculations that the total share of the field area compacted by the front wheels of the ACROS 550 combine within the accepted allowable pressure is 21.4%, over the allowable pressure - 5.8%. The obtained values may vary depending on the value of the lowest field moisture capacity of the soil in the zone of application of the combine (when climatic conditions change). | Цель исследования – разработка способов оценки относительной длины прохода комбайна и площади поля, на почву которой колеса зерноуборочного комбайна действуют сверх регламентированного допустимого предела. При разработке способа оценки уплотнения почвы ведущими колесами комбайна сверх допустимых пределов исходили из следующих положений: весовым характеристикам комбайна с пустым и заполненным бункером соответствуют индивидуальные показатели максимального давления, которые могут быть определены посредством развесовок и последующих расчетов; в процессе работы комбайна при поступлении зерна в бункер каждому значению длины маршрута соответствует определенное значение максимального давления колеса на почву, поэтому, задаваясь значением допустимого для почвы максимального давления, регламентированного ГОСТ Р 58655, и решив уравнение, можно получить значение длины прохода, после которого максимальное давление колеса на почву будет превышать допустимое стандартом значение; нахождение данной точки позволит разграничить отрезок длины прохода комбайна с допустимым давлением на почву от отрезка со сверхнормативным давлением и оценить переуплотняемую площадь поля сверх допустимого значения, что обеспечивается при выполнении ряда последовательных действий. Представленный способ расчета позволяет определить относительную площадь поля, уплотняемую зерноуборочным комбайном сверх допустимых пределов максимального давления на почву. Расчетами установлено, что общая доля площади поля, уплотняемая передними колесами комбайна ACROS 550 в пределах принятого допустимого давления, составляет 21,4%, сверх допустимого давления – 5,8%. Полученные значения могут меняться в зависимости от величины наименьшей полевой влагоемкости почвы в зоне применения комбайна (при изменении климатических условий).

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Central Scientific Agricultural Library - Russian Federation

2023

A. S. Sayganov | V. K. Lipskaya | А. С. Сайганов | В. К. Липская

The system analysis of the term «service life» is carried out, the positive and negative consequences of the use of agricultural machinery beyond its service life are determined. Under the full compliance with the measures identified, it will be possible to extend the standard life of combines without harming farmers in the form of increased crop losses, as well as to obtain additional benefits. | Выполнен системный анализ термина «срок службы», определены положительные и негативные последствия использования сельскохозяйственной техники за пределами ее срока службы. Выявлены меры, при безусловном соблюдении которых станет возможным продлить нормативный срок эксплуатации комбайнов без ущерба для аграриев в виде увеличения потерь урожая, а также получить дополнительную выгоду.

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National Academy of Sciences of Belarus - Belarus

2023

Mikhal'tsov, E.M. | Kem, A.A. | Damanskij, R.V. | Schmidt, A.N.

Cultivating cereal crops and grain legumes in Russia is not supported by the production of domestic combine harvesters capable of ensuring the organization of harvesting operations with a high level of efficiency in the whole range of yields typical for the conditions of the country. An analysis of the main characteristics of modern combine harvesters produced by the Rostselmash plant was carried out. A need to improve the range of combine harvesters produced in the Russian Federation was revealed. The calculation and graphic method has shown that in the line of domestic combine harvesters there are no models designed for harvesting cereals and grain legume crops having yield less than 1.7 t/ha and from 2.9 to 3.5 t/ha. The absence of these models defines the growth of production costs of grain crops at farms, for which such a yield is typical. It has been established that harvesting grain and leguminous crops of different yields requires a range of 5 models of combine harvesters. Each of them will have 100% use and maximum efficiency of utilization in the yield range, for which it is designed. The range of models, built according to the suggested principle, will provide satisfying the requirements of any farm in combines engaged in harvesting cereal crops and grain legumes with yields from 0.87 to 10 t per ha. The computed graphic approach facilitates the selection of a rational combine harvester model suitable for local conditions by the criterion of maximum loading of the threshing and separating device. | Возделывание зерновых и зернобобовых культур в России не подкреплено производством отечественных зерноуборочных комбайнов, способных обеспечить организацию уборочных работ с высоким уровнем эффективности во всем диапазоне урожайности, характерной для условий страны. Провели анализ основных характеристик современных зерноуборочных комбайнов производства завода "Ростсельмаш". Выявили необходимость доработки номенклатуры производимых в Российской Федерации зерноуборочных комбайнов. Расчетно-графическим методом показали, что в линейке зерноуборочных комбайнов отечественного производства отсутствуют модели, рассчитанные на уборку зерновых и зернобобовых культур с урожайностью менее 1,7 т/га и от 2,9 до 3,5 т/га. Отсутствие этих моделей обусловливает рост себестоимости производства зерновых в хозяйствах, для которых такая урожайность является типичной. Установлено, что для уборки зерновых и зернобобовых культур различной урожайности достаточно иметь линейку из 5 моделей зерноуборочных комбайнов. При этом каждая из них будет иметь 100%-ную загрузку и максимальную эффективность применения в том диапазоне урожайности, на который она рассчитана. Линейка моделей, выстроенная по предлагаемому принципу, обеспечит удовлетворение потребностей любого хозяйства в комбайнах, задействованных на уборке зерновых и зернобобовых культур с урожайностью от 0,87 до 10 т/га. Расчетно-графический метод облегчает подбор рациональной модели комбайна, подходящей к местным условиям по критерию максимальной загрузки молотильно-сепарирующего устройства.

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Central Scientific Agricultural Library - Russian Federation

2022

Stettmer, Matthias | Mittermayer, Martin | Maidl, Franz-Xaver | Schwarzensteiner, Jürgen | Hülsbergen, Kurt-Jürgen | Bernhardt, Heinz

In this study, three digital, site-specific, yield-mapping methods for winter wheat were examined, and their precision was evaluated. The crop yields of heterogeneous fields at three locations were determined on a site-specific basis using a yield-recording system composed of a combine harvester and algorithms based on reflection measurements made via satellites, as well as a tractor-mounted sensor. As a reference, the yield was determined with a plot harvester (ground truth data). The precision of the three methods was evaluated via statistical indicators (mean, median, minimum, maximum, and standard deviation) and correlation analyses between the yield of the ground truth data and the respective method. The results show a yield variation of 4.5–10.9 t ha⁻¹ in the trial fields. The yield of the plot harvester was strongly correlated with the yield estimate from the sensor data (R² = 0.71–0.75), it was moderately correlated with the yield estimate from the satellite data (R² = 0.53–0.68), and it ranged from strongly to weakly correlated with the yield map of the combine harvester (R² = 0.30–0.72). The absolute yield can be estimated using sensor data. Slight deviations (<10%) in the absolute yield are observed with the combine harvester, and there are clear deviations (±48%) when using the satellite data. The study shows differences in the precision and accuracy of the investigated methods. Further research and optimization are urgently needed to determine the exactness of the individual methods.

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National Agricultural Library - United States of America

2022

Li, Yang | Xu, Lizhang | Lv, Liya | Shi, Yan | Yu, Xun

In order to realize the demand for high-quality and high-efficiency harvest in modern agriculture, the grain combine harvesters must have the ability to intelligently adjust the operation parameters. The difficult problem is to establish the multi-parameter control system model for threshing and cleaning devices. The threshing and cleaning devices are located in the same rack space, and the interaction mechanism among agricultural material movement, mechanical structure, and airflow field is very complex. In view of the difficulties in the theoretical modeling of threshing and cleaning devices, a large number of operating parameters and performance indicators, strong coupling, and high requirements for real-time control, the system identification method was used to model the threshing and cleaning system in this paper. Firstly, the amplitude modulated PRBS input signals were designed as the input parameters of the system identification test, and the output signals acquisition test was carried out in the field. Then, the multi-input and multi-output signals of the system were used as training data, and the fusion method of the PSO (particle swarm optimization) algorithm and WNN (wavelet neural network) was proposed to identify it, and the optimal state-space model was obtained. Finally, the model identification and verification experiments were carried out on the threshing and cleaning system of various crops during the actual harvest. The VAF (variance-accounted-for) values of system identification model verification results were greater than or equal to 81.7%, and the RMSE (root mean square error) values were less than or equal to 0.602. The modeling method has high accuracy and adaptability, which laid a good foundation for realizing multi-parameter coordinated control of threshing and cleaning devices.

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National Agricultural Library - United States of America

2022

Bebie, Maria | Cavalaris, Chris | Kyparissis, Aris

Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression model (VI-MLR) based on vegetation indices (EVI, NMDI) was used. In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods were used as input parameters in three machine learning model algorithms, i.e., random forest (RF), k-nearest neighbors (KNN), and boosting regressions (BR). Modeling results were examined against yield data collected by a combine harvester equipped with a yield mapping system. VI-MLR showed a moderate performance with R² = 0.532 and RMSE = 847 kg ha⁻¹. All machine learning approaches enhanced model accuracy when all images during the growing periods were used, especially RF and KNN (R² > 0.91, RMSE < 360 kg ha⁻¹). Additionally, RF and KNN accuracy remained high (R² > 0.87, RMSE < 455 kg ha⁻¹) when images from the start of the growing period until March, i.e., three months before harvest, were used, indicating the high suitability of machine learning on Sentinel-2 data for early yield prediction of durum wheat, information considered essential for precision agriculture applications.

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National Agricultural Library - United States of America