Search

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking. [ABSTRACT FROM AUTHOR]

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods. [ABSTRACT FROM AUTHOR]

Deep learning methods have been widely used in applications of yield estimation using remote sensing data. However, they still face some challenges in terms of accuracy due to their inability to fully utilize the spatiotemporal information of remote sensing data. In response to this problem, a winter wheat yield estimation method based on ensemble deep learning and Gaussian process (GP) was proposed. First, the long-short term memory (LSTM) network and convolutional neural network (CNN) were constructed to explore deep spatiotemporal features from Sentinel-1A time-series images. Then, a GP component was applied for fusion of intraimage deep spatiotemporal features and inter-sample spatial consistency features. Finally, the yield estimation results were obtained. The experimental results showed that the proposed method had higher accuracy than those compared models, with a coefficient of determination (${R^2}$ R 2 ) of 0.698, a root mean square error (RMSE) of 477.045 kg/ha and a mean absolute error (MAE) of 404.377 kg/ha, demonstrating the application potential of the proposed method in crop yield estimation applications. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R2 of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R2 of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting. [ABSTRACT FROM AUTHOR]

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model's feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model's detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management. [ABSTRACT FROM AUTHOR]

Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making. [ABSTRACT FROM AUTHOR]

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop's growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture. [ABSTRACT FROM AUTHOR]

In precision agricultural analysis, the remote sensing of geospatial data holds substantial potential for multi-purpose crop surveys, targeting automatic crop area delineation, health monitoring, and yield estimation. Advanced remote sensing methods, when integrated with machine learning techniques, have significantly advanced agricultural analyses. This study introduces a three-tiered framework. Firstly, orchard areas are delineated using the ESA Sentinel-2 multispectral instrument (MSI) at 10 m resolution, employing the normalized differential vegetation index (NDVI). In the second stage, mango tree canopies are detected from hand-annotated true color composite imagery using two variants of a convolutional neural network. The first variant, CanopyNet-1, is built directly over RetinaNet foundational layers, achieving a mean average precision (mAP) of 0.79, a precision of 0.80, and a recall of 0.76. The second variant, CanopyNet-2, builds upon DeepForest, a generalized tree canopy trained model, also using RetinaNet at its base. CanopyNet-2 demonstrates superior performance, achieving a mAP of 0.83, a precision of 0.98, and a recall of 0.96, notably surpassing conventional models such as YOLOv5 and Faster R-CNN. Lastly, the health of the orchard is characterized using 3-m-resolution multispectral imagery. Cumulatively, our framework, with its tiered approach, exhibits high accuracy in both tree canopy delineation and health characterization, suggesting it as a comprehensive solution for large-scale orchard monitoring and yield optimization. [ABSTRACT FROM AUTHOR]

In seismic nuclear monitoring, attenuation models are important prerequisites for reliably estimating the explosive yield in an uncalibrated region without the occurrence of standard events. The seismic moment obtained by fitting source spectra is related to the source energy. This approach is appropriate for estimating yield, as the attenuation effects on the propagation path can be accurately considered. In this study, we collected 2022 vertical component waveforms in and around the Korean Peninsula from May 2010 to May 2022 to construct a high-resolution broadband Lg-wave attenuation model and inverted the Lg-wave source excitation spectra of the nuclear explosion simultaneously with attenuation correction. We obtained the scalar seismic moments by fitting the theoretical source spectra based on the Brune (J. Geophys. Res., 1970, 75, 4997-5009) model. Under the given emplacement conditions and burial depths, the seismic moments can be used to estimate yields of the North Korean nuclear tests, which are 4.6, 8.5, 19.9, 20.9, 24.7, and 337.4 kt for six nuclear explosions that occurred between 2006 and 2017. Our results are consistent with those obtained from previous teleseismic observations. [ABSTRACT FROM AUTHOR]

Pearl millet (Pennisetum glaucum L.R.Br.), is the most widely cultivated food crop after rice, wheat, and maize. The aim of the project is to determine the crop acreage of Pearl millet (Bajra) using Sentinel-1A SAR data and Machine Learning Algorithm to determine the yield estimation of the Pearl millet crop at the tehsil level using the Monteith approach. The classification overall accuracy is found to be 86.48% for Agra district and 80.15% for Firozabad district. The Relative Deviation of yield estimation for the Agra and Firozabad districts is found to be 10.14 and 6, respectively. [ABSTRACT FROM AUTHOR]

• Integration of Sentinel 1 and Sentinel 2 for time-series analysis of soybean yield prediction. • Utilized machine learning techniques to monitor crop growth during peak phenological periods. • Incorporated environmental data, Topographic Wetness Index (TWI), for improved yield predictions. • Applied correlation-based feature selection to identify key features among the predictors. It is crucial to accurately and timely estimate crop yield within field variability for sustainable management and precision farming applications. Various Earth observation systems have been developed for crop monitoring and yield prediction. However, there is a need for further research that integrates multiplatform data, advances in satellite technology, and data processing to apply this knowledge to agricultural practices. The integration of satellite imagery and environmental data has been used increasingly in recent years to predict crop yields using machine learning techniques. In recent years, VIs derived from optical satellites, particularly Sentinel 2 (S2), have gained popularity, but their availability is affected by weather conditions. On the other hand, the backscatter data from Sentinel 1 (S1) is less commonly used in agriculture due to its complex interpretation and processing, but it is not influenced by the weather. This study aims to improve the accuracy of yield predictions by combining remote sensing data with environmental variables. The use of satellite data S1 and S2 was used to identify the optimal phenological period, and a training model was developed using four machine learning techniques, including Random Forest Regression (RF), K Nearest Neighbor (KNN), Multiple Linear Regression (MLR) and Decision Tree (DT). The results showed that RF provided the highest values among the four techniques. The validation process using RF demonstrated high accuracy rates, with R2 ranging from 0.41 to 0.89, the mean square error of the root (RMSE) ranging from 0.122 to 0.224 t/ha, and the mean absolute error (MAE) ranging from 0.089 to 0.163 t/ha. The integration of satellite data S1 and S2 with topographical information may be useful for monitoring, mapping, and forecasting crop yields on small and fragmented farmlands. This approach can provide farmers, agricultural businesses, and policymakers with accurate and timely predictions of crop yield, which can facilitate decision making and provide early warnings for potential crop losses. [ABSTRACT FROM AUTHOR]

Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest operations efficiently. Vision systems for automated orchard yield estimation have received growing attention toward fruit identification with different maturity stages. However, due to interfering factors such as varying outdoor illuminations, similar colors with the surrounding canopy, imaging distance, and occlusion in natural environments, it remains a serious challenge to develop reliable visual methods for identifying blueberry fruits with different maturities. This study constructed a YOLO-BLBE (Blueberry) model combined with an innovative I-MSRCR (Improved MSRCR (Multi-Scale Retinex with Color Restoration)) method to accurately identify blueberry fruits with different maturities. The color feature of blueberry fruit in the original image was enhanced by the I-MSRCR algorithm, which was improved based on the traditional MSRCR algorithm by adjusting the proportion of color restoration factors. The GhostNet model embedded by the CA (coordinate attention) mechanism module replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-BLBE model. The BIFPN (Bidirectional Feature Pyramid Network) structure was applied in the neck network of the YOLO-BLBE model, and Alpha-EIOU was used as the loss function of the model to determine and filter candidate boxes. The main contributions of this study are as follows: (1) The I-MSRCR algorithm proposed in this paper can effectively amplify the color differences between blueberry fruits of different maturities. (2) Adding the synthesized blueberry images processed by the I-MSRCR algorithm to the training set for training can improve the model's recognition accuracy for blueberries of different maturity levels. (3) The YOLO-BLBE model achieved an average identification accuracy of 99.58% for mature blueberry fruits, 96.77% for semi-mature blueberry fruits, and 98.07% for immature blueberry fruits. (4) The YOLO-BLBE model had a size of 12.75 MB and an average detection speed of 0.009 s. [ABSTRACT FROM AUTHOR]

Remote sensing and machine learning are widely used to estimate crop yield. The use of these technologies for yield estimation of bulbous vegetables is challenging because the yield is underground and can't be directly monitored by remote sensing images. Among the bulbous vegetables, garlic (Allium sativum L.) is one of the most widely cultivated in the world. The aim of this study was to develop an accurate and transferable machine learning model to monitor and to estimate garlic yield using unmanned aerial vehicle (UAV) multispectral images. Data were collected over three growing seasons (2021, 2022, and 2023) at four different garlic phenological phases (202, 405, 407, and 409 of BBCH). The random forest (RF) algorithm was used to estimate the garlic yield by comparing two different training feature sets: the vegetation indices (VIs) and the VIs with the addition of the texture features extracted from the UAV images. The most important VIs were selected using the recursive feature elimination algorithm. Two estimation methods were compared: a direct bulb estimation and an indirect bulb estimation using the aboveground biomass as a proxy. To evaluate the transferability of the RF models, two cross-validation strategies were compared: a nested leave-one-fold-out cross-validation (LOFOCV) and a leave-one-year-out cross-validation (LOYOCV). The best performance was achieved by the direct bulb estimation using the LOFOCV strategy. Regarding the transferability of the RF models between years (i.e. LOYOCV), the indirect estimation method showed a higher transferability than the direct estimation method. Finally, the addition of texture features improved the accuracy of the RF models, but in general, their contribution was poor. This study demonstrated that the yield of bulbous vegetables can be accurately estimated by remote sensing, and that UAVs are a suitable tool to provide rapid and reliable support for garlic yield monitoring.

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

CreditScore combines the predictive power of crop growth models with future climatic scenarios, satellite images, and market data to form a comprehensive profile for each farmer-borrower, based on the future yields of their crops, with the ultimate goal of assessing long-term risks affecting yields which are related to climate change. The objective of this study is to present the tools and datasets that are employed operationally by CreditScore for future yield and profitability assessments. A modeling approach built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology is tested and evaluated in terms of data intensiveness for the prediction of wheat and cotton yields. AquaCrop, a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings introduce a promising framework that can support the financial institutions in evaluating potential customers' agribusinesses prior to and throughout the lending process. [ABSTRACT FROM AUTHOR]

This study presents an approach to address the challenges of recognizing the maturity stage and counting sweet peppers of varying colors (green, yellow, orange, and red) within greenhouse environments. The methodology leverages the YOLOv5 model for real-time object detection, classification, and localization, coupled with the DeepSORT algorithm for efficient tracking. The system was successfully implemented to monitor sweet pepper production, and some challenges related to this environment, namely occlusions and the presence of leaves and branches, were effectively overcome. We evaluated our algorithm using real-world data collected in a sweet pepper greenhouse. A dataset comprising 1863 images was meticulously compiled to enhance the study, incorporating diverse sweet pepper varieties and maturity levels. Additionally, the study emphasized the role of confidence levels in object recognition, achieving a confidence level of 0.973. Furthermore, the DeepSORT algorithm was successfully applied for counting sweet peppers, demonstrating an accuracy level of 85.7% in two simulated environments under challenging conditions, such as varied lighting and inaccuracies in maturity level assessment. [ABSTRACT FROM AUTHOR]

Establishing the yield estimation model of staple crops in Heilongjiang Province has a positive effect on grasping the agricultural situation of this province in time, and can provide a method reference for the yield estimation modeling in other regions. Firstly, the trend yield, agricultural climate indicators, remote sensing indicators, and planting fluctuation indicators were calculated. Then, yield estimation zones were established for the three crops ( soybean, corn, and rice), and modeling variables were selected using a combination of random forest and leave one method within each zone. Finally, the maximum, minimum, mean, and root mean square error of the mean absolute relative precision (MARE) of all models established based on the retention method are calculated to reflect the accuracy and stability of the final modeling. The results show that Heilongjiang Province is divided into 3, 3, and 4 yield estimating zones for soybeans, rice, and corn, respectively. The variable screening results show that the importance of trend yield > normalized difference vegetation index(NDVI) anomaly > accumulated temperature anomaly > precipitation Z-index. The MARE of the final models established for the three crops are all less than 10%, with the exception of corn yield estimation zone 1. The variance of MARE of all models is less than 0. 05% . The model established can accurately predict the crop yield under different agroclimatic conditions, and can provide a method reference for fine screening variables for yield estimation modeling. [ABSTRACT FROM AUTHOR]

Accurate and reliable yield forecasting is required for efficient planning and management of an important crop like apple. Efforts have been made to predict apple yield, mostly through the use of statistical tools with limited indicator parameters. The proposed neural network (NN) based system predicts yield of apple crops in an orchard based on identification, characterization, time of arrival and duration (ICTD) of phenological phases interactively with soil and weather parameters. The task of automatic yield estimation in orchards is challenging. Despite the significant amount of work that has been put into developing automated methods for estimating yields, the majority of methods currently in use are based on fruit counting, which is only useful one to four weeks before harvest. Whereas, in the proposed system, we will be predicting yield, during each phenological phase, among five classes, taking into account time of phenological stage occurrence (i.e. early occurrence, normal occurrence, or delay occurrence), soil parameter, and parameter related to weather conditions. This model will help the growers to timely take decision to execute contingency plans in case of average or negligible yield. The F-measure of the proposed system is 0.94 and with 95% accuracy. It is compared with other popular machine learning (ML) algorithms like Logistic regression, Support vector machines (SVM) and K-nearest neighbors (KNN). [ABSTRACT FROM AUTHOR]

Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R2 of 0.827 with selected features, while XGBoost had the best R2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops. [ABSTRACT FROM AUTHOR]

The yield estimation using artificial intelligence is based on object detection algorithms. Firstly, the object detection algorithms identify the number of fruits on images, then tree fruit load is predicted using regression algorithms. YOLO is a popular convolution neural network architecture for object detection tasks. It is sufficiently well studied for fruit yield estimation. However, the experiments are traditionally restricted to only one specific fruit category and growing season. This is a big shortcoming for the smart solutions like agro-drones, which must automatically complete yield monitoring of the most popular fruit species in commercial orchards. Therefore, the modern studies related to yield estimation increasingly raise attention to multi-stage, multi-state and multi-specie detection tasks. The multi-stage datasets can be described as a collection of multiple sub-datasets, e.g. flowers, fruitlets and fruits. The multi-state dataset can contain classes like mature, immature or damaged fruits. Meanwhile, the multi-specie dataset contains images with representatives of multiple cultures. However, if classic object-detection tasks like urban or indoor object detection have multiple classes presented in one image, then yield estimation datasets usually have images with only one class presented on them. Therefore, an image shuffle or mosaic augmentation are the intuitive training strategies of YOLO for object detection working with a collection of multiple single class datasets. We applied the YOLOv5m model to test both strategies, which were verified on three datasets: apple fruits (MinneApple), pear fruits (Pear640) and pear fruitlets (PFruitlet640). Our experiment showed that mosaic augmentation improves mAP@0.5:0.95 better than simple image shuffle. The mean difference between both strategies is equal to 0.0438. [ABSTRACT FROM AUTHOR]

Estimating wheat yield accurately is crucial for efficient agricultural management. While canopy spectral information is widely used for this purpose, the incorporation of canopy volumetric features (CVFs) remains underexplored. This study bridges this gap by utilizing unmanned aerial vehicle (UAV) multispectral imaging to capture images and elevation data of wheat at key developmental stages—gestation and flowering stages. We innovatively leveraged the elevation differences between these stages to calculate canopy height, develop a novel CVF, and refine the wheat yield prediction model across various wheat varieties, nitrogen fertilizer levels, and planting densities. The integration of canopy volume information significantly enhanced the accuracy of our yield prediction model, as evidenced by an R2 of 0.8380, an RMSE of 313.3 kg/ha, and an nRMSE of 11.33%. This approach not only yielded more precise estimates than models relying solely on spectral data but also introduced a novel dimension to wheat yield estimation methodologies. Our findings suggest that incorporating canopy volume characteristics can substantially optimize wheat yield prediction models, presenting a groundbreaking perspective for agricultural yield estimation. [ABSTRACT FROM AUTHOR]

Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In seismic nuclear monitoring, attenuation models are important prerequisites for reliably estimating the explosive yield in an uncalibrated region without the occurrence of standard events. The seismic moment obtained by fitting source spectra is related to the source energy. This approach is appropriate for estimating yield, as the attenuation effects on the propagation path can be accurately considered. In this study, we collected 2022 vertical component waveforms in and around the Korean Peninsula from May 2010 to May 2022 to construct a high-resolution broadband Lg-wave attenuation model and inverted the Lg-wave source excitation spectra of the nuclear explosion simultaneously with attenuation correction. We obtained the scalar seismic moments by fitting the theoretical source spectra based on the Brune (J. Geophys. Res., 1970, 75, 4997–5009) model. Under the given emplacement conditions and burial depths, the seismic moments can be used to estimate yields of the North Korean nuclear tests, which are 4.6, 8.5, 19.9, 20.9, 24.7, and 337.4 kt for six nuclear explosions that occurred between 2006 and 2017. Our results are consistent with those obtained from previous teleseismic observations.

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ' b 1 − b 2 / b 3 − b 4 ' derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale. [ABSTRACT FROM AUTHOR]

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response. [ABSTRACT FROM AUTHOR]

Fruit counting, as one of the essential parts of yield estimation, is an important factor in production process planning. In the case of apple crops, it is useful in orchard management and as guidance for farmers, showing a decisive role in product market strategies and cultivation practices. Although some machine vision based studies have exhibited notable fruit counting ability, they still need to be improved for clustered fruit. This study proposes an automatic fruit counting pipeline called twice matched fruit counting system to overcome this limitation. The twice matched fruit counting system consists of three sub-algorithms: i) object detection model based on You Only Look Once Version 4-tiny; ii) fruit tracking with mutual match; iii) and fruit counting with ID assignment. The object detection model was developed based on You Only Look Once Version 4-tiny, which quickly and accurately detect fruit and trunks with mean average precision of 96.4% and detection speed of 16 ms. The fruit tracking with mutual match was designed to alleviate match errors associated with the clustered fruit, which achieved superior performance with average ID Switch Rate of 3.9%, Multiple Object Tracking Accuracy of 89.9% and Multiple Object Tracking Precision of 93.5%. The fruit counting was implemented by ID assignment, where each fruit was assigned with a unique ID based on fruit tracking results and direction of camera motion. The root mean squared error and coefficient of determination were 16.3 fruit per video and 0.93, respectively, which indicate a high correlation between fruit count results from the proposed approach and ground truth counting results. The twice matched fruit counting system was implemented on Central Processing Unit at 3–5 frames per second. These results demonstrate a potential of the twice matched fruit counting system for estimating fruit yield in modern apple orchards, which could provide technical support for orchard management. • Twice Matched Fruit Counting System implemented video-based fruit counting. • Target fruit detection influenced by fruit on the ground or in the back row of tree. • Mutual match algorithm alleviated ID switch caused by clustered fruit. • Fruit motion trajectories became smoother after removing abnormal matches. • ID assignment efficiently counted fruits in modern apple orchards. [ABSTRACT FROM AUTHOR]

Infructescence detection during the early fruiting stage is a necessary preliminary work to estimate the yield of Zanthoxylum. The purpose of this research is to detect and quantify the infructescences on the images of early fruit-bearing branches of Zanthoxylum which are collected in the natural environment. Thus, a machine vision-based algorithm for detecting Zanthoxylum infructescences is proposed, which contains of two phases. The first is to segment fruit-like and non-fruit region from Zanthoxylum branch image by extracting the Histogram of Oriented Gradient (HOG) feature map and Excess Green minus Excess Red (ExGR) index to obtain the fruits of Zanthoxylum infructescences. The second is to identify fruits adaptively and roughly in accordance with the density of their position distribution, then an optimization model is established to merge rough clusters and the optimal clustering result is obtained. Experiments with normal samples demonstrate that the proposed approach receives a Precision of 96.67%, a Recall of 91.07%, and an F1-score of 0.93. Compared with ADPC-kNN, DBSCAN and OPTICS, the results reveal that the proposed algorithm has an improved robustness and achieves higher Recall and F1-score. Meanwhile, its competitiveness is displayed in the experiments with deep learning-based methods. It can adaptively detect the infructescences of Zanthoxylum branch images, and the experiments prove that it is effective.

Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.

Abstract Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale.

Abstract Estimating wheat yield accurately is crucial for efficient agricultural management. While canopy spectral information is widely used for this purpose, the incorporation of canopy volumetric features (CVFs) remains underexplored. This study bridges this gap by utilizing unmanned aerial vehicle (UAV) multispectral imaging to capture images and elevation data of wheat at key developmental stages—gestation and flowering stages. We innovatively leveraged the elevation differences between these stages to calculate canopy height, develop a novel CVF, and refine the wheat yield prediction model across various wheat varieties, nitrogen fertilizer levels, and planting densities. The integration of canopy volume information significantly enhanced the accuracy of our yield prediction model, as evidenced by an R2 of 0.8380, an RMSE of 313.3 kg/ha, and an nRMSE of 11.33%. This approach not only yielded more precise estimates than models relying solely on spectral data but also introduced a novel dimension to wheat yield estimation methodologies. Our findings suggest that incorporating canopy volume characteristics can substantially optimize wheat yield prediction models, presenting a groundbreaking perspective for agricultural yield estimation.

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model’s feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model’s detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management.

Agriculture in free spaces within urban areas sustains residents as they grow crops to ensure food availability while at the same time selling other produce to fund other needs. Due to the value of urban agricultural production, the main focus of this study was to investigate the implications of urban growth on maize yield in Harare metropolitan city. In order to achieve this, Landsat multispectral and multi-temporal data were used to establish the responses of the estimated maize yield to city growth from 1984 to 2018. Initially, Land Use and Land Cover map for each period were produced using multispectral images and field observation in Support Vector Machine (SVM)-based supervised image classification. The maps were reclassified using a binary scheme of croplands and non-croplands, which was used to quantify cropland area in each period and hence change over time. The maize yield for each period was estimated from data obtained from a study of maize yields obtained at the University of Zimbabwe Farm (UZ) farm for maize grown under variable agricultural practices. Results showed that cropland area was reduced from 11,120 ha in 1984 down to 2631 ha in 2018. The estimated average maize yield decreased from 52,264 tons in 1984 to 12,366 tons in 2018. In addition to showing the value of urban agriculture, the findings of this study are important in informing the government, municipalities, and other stakeholders about how urban growth has the potential to compromise food security and livelihoods, especially for the urban poor. [ABSTRACT FROM AUTHOR]

The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits. • An end-to-end trainable multi-task deep neural network was designed. • The network includes two branches: instance segmentation and size regression. • The architecture was adapted to be used with 4-channel RGB + D images. • Fruit detection results reported an F1-score of 0.88. • Fruit size estimation results reported a mean absolute error of 5.64 mm. [ABSTRACT FROM AUTHOR]

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92. [ABSTRACT FROM AUTHOR]

Introduction: To stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation. Methods: A field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield. Results: For the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales. Discussion: The results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage. [ABSTRACT FROM AUTHOR]

Yield estimation is an important field of study in agriculture. Forecasting yields provides producers, consumers, traders and policymakers with important preliminary information and time to take necessary action. Corn is an important product in terms of international trade and is widely used in human and animal nutrition throughout the world. Adana produces the highest amount of corn sown both as main and secondary product in Türkiye. Therefore, in this study, corn yield was tried to be estimated by using various meteorological parameters and plant fertilizer usage amounts. For this purpose, statistical (Auto-ARIMA), machine learning (Random Forest) and deep learning (CNN, LSTM) methods were used. The study findings showed that all models used predicted maize yield highly accurately. However, the highest accuracy LSTM model estimated the yield of first corn crop. [ABSTRACT FROM AUTHOR]

Precision crop load management of apple requires counting fruiting structures at various times during the year to guide management decisions. The objective of the current study was to evaluate the accuracy of and compare different commercial computer vision systems and computer applications to estimate trunk cross-sectional area (TCSA), flower cluster number, thinning efficacy, and yield estimation. These studies evaluated two companies that offer different vision systems in a series of trials across 23 orchards in four states. Orchard Robotics uses a proprietary camera system, and Pometa (previously Farm Vision) uses a cell phone camera system. The cultivars used in the trials were 'NY1', 'NY2', 'Empire', 'Granny Smith', 'Gala', 'Fuji', and 'Honeycrisp'. TCSA and flowering were evaluated with the Orchard Robotics camera in full rows. Flowering, fruit set, and yield estimation were evaluated with Pometa. Both systems were compared with manual measurements. Our results showed a positive linear correlation between the TCSA with the Orchard Robotics vision system and manual measurements, but the vision system underestimated the TCSA in comparison with the manual measurements (R2s between 0.5 and 0.79). Both vision systems showed a positive linear correlation between nubers of flowers and manual counts (R2s between 0.5 and 0.95). Thinning efficacy predictions (in June) were evaluated using the fruit growth rate model, by comparing manual measurements and the MaluSim computer app with the computer vision system of Pometa. Both systems showed accurate predictions when the numbers of fruits at harvest were lower than 200 fruit/tree, but our results suggest that, when the numbers of fruits at harvest were higher than 200 fruit/tree, both methods overestimated final fruit numbers per tree when compared with final fruit numbers at harvest (R2s 0.67 with both systems). Yield estimation was evaluated just before harvest (August) with the Pometa system. Yield estimation was accurate when fruit numbers were fewer than 75 fruit per tree, but, when the numbers of fruit at harvest were higher than 75 fruit per tree, the Pometa vision system underestimated the final yield (R2 = 0.67). Our results concluded that the Pometa system using a smartphone offered advantages such as low cost, quick access, simple operation, and accurate precision. The Orchard Robotics vision system with an advanced camera system provided more detailed and accurate information in terms of geo-referenced information for individual trees. Both vision systems evaluated are still in early development and have the potential to provide important information for orchard managers to improve crop load management decisions. [ABSTRACT FROM AUTHOR]

Cotton is under the threat of climate and ecosystem change and has an essential role in the global textile industry. This makes its yield prediction essential for both economics and sustainability. The potential cotton yield can be predicted by integrating climatic factors, soil parameters, and biophysical parameters observed by high temporal and spatial resolution remote sensing satellites. This study used a multisource dataset to create an explainable and accurate predictive model for cotton yield prediction over the continental United States (CONUS). A recently proposed glass-box method called explainable boosting machine (EBM), which provides transparency, reliability, and ease of interpretation, was implemented. Accuracy performance was compared with the common machine learning (ML) methods for predicting cotton yields. The EBM showed higher accuracy against other glass-box methods and competitive results with black-box models. With the help of the EBM, the importance of individual features and their pairwise interactions was revealed without applying any post hoc methods. The study findings showed that precipitation (P), enhanced vegetation index (EVI), and leaf area index (LAI) are the three most important dynamic features. The dynamic features are the driver of the created model with 78% of the overall feature importance, followed by pairwise interactions of the features with 16% contribution. Finally, static features contribute 6% to the overall feature importance. The study highlights the importance of using multisource data and interactions of the input features and providing an interpretable model to understand the inner dynamics of cotton yield predictions. [ABSTRACT FROM AUTHOR]

Food security is at stake, with climate change heavily impacting agriculture and food production. In the present context of extreme events and changing conditions, developing advanced crop yield models can learn from all available information, and providing interpretable predictions for decision-making is of paramount relevance. This work explores the potential and limitations of developing interpretable crop yield models using long short-term memory (LSTM) neural networks, which typically excel at extracting information from time series. LSTMs were designed and trained with multisource satellite and meteorological time series over Continental US (CONUS) and corn, soybean, and wheat yield data from the US Department of Agriculture. Two recent attribution methods are used to interpret and extract knowledge from the developed models: integrated gradients (IG), based on back-propagation, and Shapley (SHAP) values, based on perturbations. Our results show that: 1) LSTM models achieved high accuracy ($\text {R}^{2}>0.56$); 2) multisource combinations outperformed single-variable models in all crop models; 3) both attribution methods were equivalent in detecting essential drivers and their contribution; 4) satellite estimates of enhanced vegetation index (EVI) and vegetation optical depth (VOD) together with meteorological estimates of maximum temperature (TMX) were the most relevant input features for crop yield estimations; and finally and 5) we discovered critical periods of the crop growth cycle for the corn, soybean, and wheat models. The suggested strategy constitutes an important step toward modeling and understanding crop production systems and advancing in automatic data-driven and accountable field management. [ABSTRACT FROM AUTHOR]

To further reveal the relationships between different variables and yield at each growth stage of winter wheat, an approach for estimating regional yields of winter wheat at multiple time scales was developed by assimilating the CERES-Wheat model simulations and remotely sensed observations. Specifically, the particle filter assimilation algorithm was chosen to assimilate the simulated soil moisture at the depth of 0–20 cm and leaf area index (LAI) and MODIS retrieved vegetation temperature condition index (VTCI) and LAI. The resonance periods of time series assimilated VTCIs and LAIs at different growth stages of winter wheat with crop yield were analyzed separately using the cross-wavelet transform to determine the variation relationships between the assimilated variables and yield at multiple time scales, and the calculated weights of assimilated VTCI and LAI at each growth stage of winter wheat were used to establish a yield estimation model. Both assimilated VTCI and LAI could comprehensively integrate the effects of the CERES-Wheat model simulations and remotely sensing observations, and cross-wavelet transformed time series VTCIs and LAIs at each growth stage had specific resonance periods with the time series yields, regardless of whether they were assimilated or not. Compared with the unassimilated variables, assimilated VTCI and LAI were given greater weights at the key growth stages, namely VTCI at the jointing and heading-filling stages and LAI at the heading-filling and milk maturity stages, enhancing feature extraction and the accuracy of yield estimation was improved.

Abstract Background The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult. Results We propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The results showed that the R2 between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. Moreover, MAE, MAPE, RMSE are only 4.18, 10.0%, 6.48 respectively, the deviation is very small. Conclusions We have achieved the first accurate counting of soybean pods and proposed a new solution for the detection and counting of dense objects.

Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest operations efficiently. Vision systems for automated orchard yield estimation have received growing attention toward fruit identification with different maturity stages. However, due to interfering factors such as varying outdoor illuminations, similar colors with the surrounding canopy, imaging distance, and occlusion in natural environments, it remains a serious challenge to develop reliable visual methods for identifying blueberry fruits with different maturities. This study constructed a YOLO-BLBE (Blueberry) model combined with an innovative I-MSRCR (Improved MSRCR (Multi-Scale Retinex with Color Restoration)) method to accurately identify blueberry fruits with different maturities. The color feature of blueberry fruit in the original image was enhanced by the I-MSRCR algorithm, which was improved based on the traditional MSRCR algorithm by adjusting the proportion of color restoration factors. The GhostNet model embedded by the CA (coordinate attention) mechanism module replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-BLBE model. The BIFPN (Bidirectional Feature Pyramid Network) structure was applied in the neck network of the YOLO-BLBE model, and Alpha-EIOU was used as the loss function of the model to determine and filter candidate boxes. The main contributions of this study are as follows: (1) The I-MSRCR algorithm proposed in this paper can effectively amplify the color differences between blueberry fruits of different maturities. (2) Adding the synthesized blueberry images processed by the I-MSRCR algorithm to the training set for training can improve the model’s recognition accuracy for blueberries of different maturity levels. (3) The YOLO-BLBE model achieved an average identification accuracy of 99.58% for mature blueberry fruits, 96.77% for semi-mature blueberry fruits, and 98.07% for immature blueberry fruits. (4) The YOLO-BLBE model had a size of 12.75 MB and an average detection speed of 0.009 s.

This study presents an approach to address the challenges of recognizing the maturity stage and counting sweet peppers of varying colors (green, yellow, orange, and red) within greenhouse environments. The methodology leverages the YOLOv5 model for real-time object detection, classification, and localization, coupled with the DeepSORT algorithm for efficient tracking. The system was successfully implemented to monitor sweet pepper production, and some challenges related to this environment, namely occlusions and the presence of leaves and branches, were effectively overcome. We evaluated our algorithm using real-world data collected in a sweet pepper greenhouse. A dataset comprising 1863 images was meticulously compiled to enhance the study, incorporating diverse sweet pepper varieties and maturity levels. Additionally, the study emphasized the role of confidence levels in object recognition, achieving a confidence level of 0.973. Furthermore, the DeepSORT algorithm was successfully applied for counting sweet peppers, demonstrating an accuracy level of 85.7% in two simulated environments under challenging conditions, such as varied lighting and inaccuracies in maturity level assessment.