Yield Monitoring System Research Articles

Easy yield mapping for precision agriculture

ABSTRACT Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture.

Research of real-time corn yield monitoring system with DNN-based prediction model.

The real-time monitoring of corn yield by a combine harvester is a critical data source for constructing the yield histogram, which significantly benefits precision management and decision-making in modern precision agriculture. While widely used, the current photoelectric sensor-based yield monitoring method has limitations. It detects the corn height on each scraper and calculates the yield through a geometric formula. However, it neglects the noticeable difference in the corn stacking patterns affected by factors such as feeding volume, terrain, and driving speed. This oversight often results in low accuracy and poor stability in the prediction of corn yield, highlighting the need for a more advanced approach. To resolve this, we employ EDEM discrete element simulation to demonstrate the large difference of corn stacking patterns on the scraper of the elevator corresponding to feeding volume. Then, we develop a real-time monitoring system on our self-developed double elevator testing rig for carrying out a composite dataset for training three machine learning algorithm-based models, namely Deep Neural Networks (DNN), Gradient Boosting Machine (GBM), and Random Forest (RF). Importantly, these models have undergone rigorous validation under various feeding volumes, ensuring their robustness and reliability. The auxiliary elevator speed is meticulously set at 150r/min, 225r/min, and 450r/min, providing a comprehensive performance assessment. The results denote that the DNN model performs best and is stable, with a coefficient of determination (R2) of 0.998, root mean square error (RMSE) of 0.526, and mean absolute error (MAE) of 0.425. The paper also performs field experiments to test the proposed three prediction models and the system. The results also denote the DNN-based prediction model's best performance for the lowest relative error of 2.29% and the highest average accuracy of 97.85%. Consequently, the proposed real-time corn yield monitoring system achieves high accuracy and reliability for the combine harvester applications.

Evaluating UAV-Based Remote Sensing for Hay Yield Estimation.

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel-1) and 50 m (35 mm pixel-1) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R2 values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps.

Design and Experiments of a Convex Curved Surface Type Grain Yield Monitoring System

Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, the objective of this research was to design a convex curved grain mass flow sensor to improve the accuracy and practicality of grain yield monitoring. In addition, it involves the development of a grain yield monitoring system based on a cut-and-flow combine harvester prototype. This research examined the real output signal of the convex curved grain mass flow sensor. Errors caused by variations in terrain were reduced by establishing the zero point of the sensor’s output. Measurement errors under different material characteristics, flow rates, and grain types were compared in indoor experiments, and the results were subsequently confirmed through field experiments. The results showed that a sensor with a cantilever beam-type elastic element and a well-constructed carrier plate may achieve a measurement error of less than 5%. After calibrating the sensor’s zero and factors, it demonstrated a measurement error of less than 5% during the operation of the combine harvester. These experimental results align with the expected results and can provide valuable technical support for the widespread adoption of impulse grain flow detection technology. In future work, the impact of factors such as vehicle vibration will be addressed, and system accuracy will be improved through structural design or adaptive filtering processing to promote the commercialization of the system.

小区联合收割机智能测产系统的设计与试验

Traditional yield monitoring in breeding plots is manually conducted, which is labour-intensive and inefficient. To address this issue, an airborne yield monitoring system that collects yield information during the harvesting in plots was designed. The system consists of a host computer and a slave computer. The slave computer used weight and moisture sensors to collect yield information of crops. The host computer used LabVIEW software to design a data management platform that displays and saves yield information and draws yield distribution maps for data visualization. The host and slave computers communicate data through 4G networks. By designing anti-interference measures, the dynamic experimental results showed that the average errors between the measured weight and moisture content by the system were 1.330% and 2.800%, which met the accuracy requirements of yield testing in plot breeding. The results indicated that the structure of the system is reasonable and can provide reliable yield monitoring tools for researchers in breeding plots.

Vibration and Slope Conditions during Harvesting Affect Radish Mass Measurements for Yield Monitoring: An Experimental Study Using a Laboratory Test Bench.

Site-specific measurements of the crop yield during harvesting are essential for successfully implementing precision management techniques. This study aimed to estimate the mass of radish tubers using the impact principle under simulated vibration and sloped-field harvesting conditions with a laboratory test bench. These conditions included the conveyor speed (CS), impact plate layout (IP), falling height onto the impact plate (FH), the plate angle relative to the horizontal (PH), the field slope, and the vibration of the harvesting machine. Two layouts of impact-type sensors were fabricated and tested, one with a single load cell (SL) and the other with two load cells (DL). An adjustable slope platform and a vibration table equipped with vibration blades were utilized to simulate the slope and vibration effects, respectively. Calibrations were conducted to verify the accuracy of the sensor outputs, processed with the finite impulse response and moving average filters. Radish mass was estimated using an asymmetrically trimmed mean method. The relative percentage error (RE), standard error (SE), coefficient of determination (R²), and analysis of variance (ANOVA) were used to assess the impact plate performance. The results indicated that the SE for both impact plates was less than 4 g in the absence of vibration and slope conditions. The R2 for the single and double impact plates ranged from 0.58 to 0.89 and 0.69 to 0.81, respectively. The FH had no significant impact, while the PH significantly affected the mass measurements for both impact plates. On the other hand, the CS significantly affected the plate performance, except for the double-load-cell impact plate. Both vibration and slope affected the mass measurements, with RE values of 9.89% and 13.92%, respectively. The RE for filtered radish signals was reduced from 9.13% to 5.42%. The tests demonstrated the feasibility of utilizing the impact principle to assess the mass of radishes, opening up possibilities for the development of yield-monitoring systems for crops harvested in a similar manner.

Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression

Accurate timely and early-season crop yield estimation within the field variability is important for precision farming and sustainable management applications. Therefore, the ability to estimate the within-field variability of grain yield is crucial for ensuring food security worldwide, especially under climate change. Several Earth observation systems have thus been developed to monitor crops and predict yields. Despite this, new research is required to combine multiplatform data integration, advancements in satellite technologies, data processing, and the application of this discipline to agricultural practices. This study provides further developments in soybean yield estimation by comparing multisource satellite data from PlanetScope (PS), Sentinel-2 (S2), and Landsat 8 (L8) and introducing topographic and meteorological variables. Herein, a new method of combining soybean yield, global positioning systems, harvester data, climate, topographic variables, and remote sensing images has been demonstrated. Soybean yield shape points were obtained from a combine-harvester-installed GPS and yield monitoring system from seven fields over the 2021 season. The yield estimation models were trained and validated using random forest, and four vegetation indices were tested. The result showed that soybean yield can be accurately predicted at 3-, 10-, and 30-m resolutions with mean absolute error (MAE) value of 0.091 t/ha for PS, 0.118 t/ha for S2, and 0.120 t/ha for L8 data (root mean square error (RMSE) of 0.111, 0.076). The combination of the environmental data with the original bands provided further improvements and an accurate yield estimation model within the soybean yield variability with MAE of 0.082 t/ha for PS, 0.097 t/ha for S2, and 0.109 t/ha for L8 (RMSE of 0.094, 0.069, and 0.108 t/ha). The results showed that the optimal date to predict the soybean yield within the field scale was approximately 60 or 70 days before harvesting periods during the beginning bloom stage. The developed model can be applied for other crops and locations when suitable training yield data, which are critical for precision farming, are available.

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters.

Development of Potato Mass Estimation System Based on Deep Learning

Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.

Design and Experiment of Real-Time Grain Yield Monitoring System for Corn Kernel Harvester

Real-time crop harvest data acquisition from harvesters during harvesting operations is an important way to understand the distribution of crop harvest in the field. Most real-time monitoring systems for grain yield using sensors are vulnerable to factors such as low accuracy and low real-time performance. To address this phenomenon, a real-time grain yield monitoring system was designed in this study. The real-time monitoring of yield was accomplished by adding three pairs of photoelectric sensors to the elevator of the corn kernel harvester. The system mainly consists of a signal acquisition and processing module, a positioning module and a visualization terminal; the signal acquisition frequency was set to 1 kHz and the response time was 2 ms. When the system operated, the signal acquisition and processing module detected the sensor signal duration of grain blocking the scrapers of the grain elevator in real-time and used the low-potential signal-based corn grain yield calculation model constructed in this study to complete the real-time yield measurement. The results of the bench tests, conducted under several different operating conditions with the simulated elevator test bench built, showed that the error of the system measurement was less than 5%. Field tests were conducted on a Zoomlion 4YZL-5BZH combined corn kernel harvester and the results showed that the average error of measured yield was 3.72%. Compared to the yield measurement method using the weighing method, the average error of the bench test yield measurement was 7.6% and the average error of yield measurement in field trials with a mass flow sensor yield measurement system was 16.38%. It was verified that the system designed in this study has high yield measurement accuracy and real-time yield measurement, and can provide reference for precision agriculture and high yield management.

Development of User Terminal Software for Korean Grain Yield Monitoring Systems

Development of User Terminal Software for Korean Grain Yield Monitoring Systems

Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation

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.

Development and application experiments of a grain yield monitoring system

An impact-type grain yield monitoring system was developed and tested in laboratory and field to meet the requirement of precision agriculture in China. The system consisted of a mass flow sensor, a clean elevator speed sensor, a ground speed sensor, a moisture content sensor, a controller area network-bus processor, a global navigation satellite system receiver, and an industrial personal computer. The system was used to collect yield data and create the grid map in a field of winter wheat from 2018 to 2019. Laboratory and field calibrations were implemented to verify the accuracy of the mass flow sensor and the moisture content sensor. The errors of time delay and parking were removed, and exponential smoothing was performed in the data preprocessing. A yield reconstruction method was proposed to calculate the yield data of the sampling points based on the empirical model between flow voltage and grain weight. The predicted errors of total yield in both years were less than 5%, which indicated that the accuracy of yield monitoring system was high. An averaging method was used to generate the yield grid map. The spatiotemporal variation of the two-year yield was analyzed. Hierarchical management for precision farming was studied according to spatial variability (CVS = 31%) in 2018. Results showed that the temporal variability was CVT = 34%, which confirmed that the yield increased by about 0.3 t compared with the previous year. Moreover, the variability of the grid map showed the distribution of the yield reduction area, which was related to the characteristics of the seeds and the regional properties of the soil. The results proved that the developed yield monitoring system can be used as a tool to provide a reference for precision agriculture and high-yield management of winter wheat.

Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

Instance segmentation of root crops and simulation-based learning to estimate their physical dimensions for on-line machine vision yield monitoring

Modern agriculture is based on control and optimization, where monitoring is essential. But yield monitoring limited to spatial mapping of biomass is unsatisfactory for uniformity optimization. Acquiring this information as soon as during the harvest could improve efficiency of precision agriculture. Current machine vision solutions do not allow this for several reasons, starting with difficulties with recognition of heavily cluttered, often mutually occluding objects, or requirements for elaborate hardware. Acquiring images in on-line conditions often results in perspective distortion which makes measurement difficult. This is even more challenging in the case of root crops, as they are usually graded basing on their minimal diameter, which is not directly observable via monocular vision.We propose a method for developing yield monitoring systems that can estimate physical dimensions of crops directly on-line. In our approach, individual plants are segmented using Mask R-CNN, allowing clear separation even in heavily cluttered conditions and in the presence of occlusions. Then, a nonlinear regression model is used to predict the minimal diameter of each plant basing on their observed contours. This model is capable of jointly correcting the perspective distortion and estimate the non-observed dimension. Training this model requires per-object annotations with their true diameters. Since this information is very difficult to acquire for real crops, we build a simulation environment where we model the crops, eventually rendering a synthetic dataset to train the regression model on. The size estimation model transfers the knowledge from simulated data, correctly predicting the sizes of real plants. We demonstrate our method on the case of potatoes, but it can be applied to other crops as well.

Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Spatial estimation methods for mapping corn silage and grain yield monitor data

Harvester-mounted yield monitor systems are increasingly used to document corn (Zea mays L.) yield. The three most commonly used spatial estimation methods to convert point data gathered with yield monitors to regular, grid-based, raster maps include nearest neighbor (NN), inverse distance weighting (IDW) and kriging. Seven spatial estimation methods (NN, IDW using 10, 20, 30 and all data points and kriging with exponential and Matern covariance functions) were evaluated to determine the method that most accurately captures intra-field spatial variability of corn silage and corn grain yield in New York. Yield monitor data from two dairy farms and two grain operations were cleaned using Yield Editor prior to spatial analyses. The dataset included 7–10 years of data per farm for a combined 7484 ha (245 fields) of silage and 6971 ha (253 fields) of grain. Data were split into training (80%) and cross-validation datasets (remaining 20% of the data). Normalized root mean squared error (NRMSE) was used to evaluate the accuracy of the spatial estimation methods. Kriging with the Matern covariance function resulted in the most accurate corn silage and grain yield raster maps both at the farm and field level. There were statistically significant differences in NRMSE between kriging with the Matern isotropic covariance function and all other models for both corn silage and grain, regardless of field size, year when data were obtained or farm that supplied the data. These results are beneficial to ensure accurate and precise spatial mapping of yield products toward optimized corn growth management.

Portable Wireless Yield Monitoring System on Conventional Rice Combine

Abstract. A simple, portable, and rugged instrumentation system has been successfully developed and field demonstrated to monitor, measure, and record the harvested crop yield and selected machine field performance parameters from the typical rice combines in Malaysia. The complete system comprises of two ultrasonic sensors located at the combine header to measure the cutting width, microwave solid flow, and microwave moisture sensors at the combine clean grain auger to measure the flow rate and moisture content of the cleaned grains going into the grain tank, electromagnetic detector on the combine grain elevator drive shaft to monitor the grain elevator rotational speed, and lastly a DGPS receiver on the combine console roof to indicate the travel speed and geo-position in the field. All these measured parameters were made to display in-real time on the touch panel screen of the embedded system on-board the combine for the interest of the combine operator and also made to display in-real time on the monitor of the toughbook at the on-ground base station for the interest of the system controller. Static calibrations on the individual sensors showed excellent measurement linearity having R2 values within 0.8760 to 1.000 ranges. The wireless communication between the embedded system on-board the combine and the toughbook at the on-ground base station could be sustained to a maximum distance of 185 m apart. Site specific variability maps of crop yield, harvested grain moisture content, combine cutting width, combine traveling speed, combine field capacity, and combine field efficiency within the harvested area could be produced from the data obtained with the instrumentation system using a GIS software. Keywords: Grain harvesting, Paddy mechanization, Precision farming, Wireless data transmission, Yield monitoring.

Development of Potato Yield Monitoring System Using Machine Vision

Development of Potato Yield Monitoring System Using Machine Vision

Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network

Agriculture stands as the backbone of our Nation, by Contributing 7% of the total Indian Economy. The drivers of agriculture are facing a huge problem in predicting the yield in different varieties of soil. Currently, the use of sophisticated technologies in the field of agriculture is underdeveloped when compared to other sectors over the past few decades. Self-mortality rates of farmers considerably increasing from the past four-five years. This is mainly due to the debt overhead usually caused by low yield. Crops yield decline considerably due to unpredictable weather, environmental changes, and diseases. This can say that agricultural landowners fear to use new technologies and tend to follow the age-old tradition of farming. The sphere of computing with its rigorous learning capabilities is inevitable to find a novel solution for agriculture-related issues. In this paper, this issue is addressed and have come up with an improvised idea to help farmers get a better yield for their crops. Deep learning has been used to predict the yield. This technology is made handy for every farmer to learn and use it effectively via a simple IoT device installed in their fields and a smartphone application. This improvised system has been named as Agriculture Yield Monitor System (AYMS). This has proved to be more efficient and beneficial to farmers.