Yield Estimation Research Articles

Fallen apple detection as an auxiliary task: Boosting robotic apple detection performance through multi-task learning

In modern agricultural practices, advanced machine learning techniques play a pivotal role in optimizing yields and management. A significant challenge in orchard management is detecting apples on trees, which is essential for effective harvest planning and yield estimation. The YOLO series, especially the YOLOv8 model, stands out as a state-of-the-art solution for object detection, but its potential in orchards remains untapped. Addressing this, our study evaluates YOLOv8’s capability in orchard apple detection, aiming to set a benchmark. By employing image augmentation techniques like exposure, rotation, mosaic, and cutout, we lifted the model's performance to a state-of-the-art level. We further integrated multi-task learning, enhancing tree apple detection by also identifying apples on the ground. This approach resulted in a model with robust accuracy across evaluation metrics. Our results underscore that the YOLOv8 model achieves a leading standard in orchard apple detection. When trained for both tree and fallen apple detection, it outperformed the one when trained exclusively for the former. Recognizing fallen apples not only reduces waste but could also indicate pest activity, influencing strategic orchard decisions and potentially boosting economic returns. Merging cutting-edge tech with agricultural needs, our research showcases the promise of multi-task learning in fruit detection with deep learning.

NVW-YOLOv8s: An improved YOLOv8s network for real-time detection and segmentation of tomato fruits at different ripeness stages

Tomato yield estimation relies significantly on the accurate detection of fruit quantity and size. And object detection and semantic segmentation of fruits emerge as efficacious methodologies for the realization of fruit counting and size detection. To address challenges associated with the detection and segmentation of tomato fruits in complex environments such as sample imbalance of different classes, small targets, and susceptibility to occlusion at varying stages of ripeness, this study proposed a foreground-foreground class balance method and an improved YOLOv8s network, NVW-YOLOv8s, for detecting and segmenting tomato fruits simultaneously. The foreground-foreground class balance method initially performed pixel-wise extraction on fruit samples with fewer instances. Subsequently, it synthesized them with original images containing a limited number of samples from the class, thereby augmenting the overall quantity of this specific category of fruit samples. In the NVW-YOLOv8s network, a C2f-N module, founded on the normalization-based attention module (NAM), was specifically crafted for residual feature learning. This design serves to augment the network's proficiency in extracting and integrating feature information pertaining to tomato fruits within intricate environments. Additionally, a variable focal loss (VFL) was introduced as the classification loss function to address the issue of positive and negative sample imbalance, and a regression loss function based on Wise-IoU (WIoU) was incorporated to tackle challenges related to fruit small targets and susceptibility to occlusion. The YOLOv8s models trained with the augmented balanced dataset revealed that the model's detection and segmentation performance, as indicated by mAP@0.5, improved by 4.8 % and 5.4 %, respectively, compared with the model trained with the augmented original training dataset. The test results on the augmented balanced dataset indicated that the proposed NVW-YOLOv8s network achieved a detection mAP@0.5 of 91.4 %, an F1-Score of 85.4 %, a segmentation mAP@0.5 of 90.7 %, and an F1-Score of 84.8 %. These results surpass the baseline YOLOv8s network by 4.3 % and 5.5 % for detection, 4.1 % and 5.0 % for segmentation, respectively. Additionally, the processing for concurrent detection and segmentation was measured at a speed of 60.2 FPS. Therefore, the proposed method has successfully met the precision and real-time requirements for intelligent yield estimation in horticultural crops.

Crop yield prediction by Mestrial Environ Netsual Network (MENN)

Crop yield prediction methods can roughly predict actual yield, although better yield prediction performance is still sought. In the existing methodologies the crop yield prediction outcomes are based on the past experience data and failed to predict the exact outcomes of the crop yield. Hence, a hybrid approach namely Crop yield prediction by Mestrial Environ Netsual Network (MENN) has been proposed to overcome the challenges in the existing approaches and to predict the crop yield with impeccable manner. In previous techniques, the change in phenotype as well as genes in the seed and the plant pathology are not combined as a new model. Hence, Mestrial Neural Network (MNN) has been proposed which consist of Task allocation layer, Subset-net layer and Integrated yield estimation layer to predict the sowing seed gene along with the phenotype and pathology. Also, incorporated pathology module examines the phenotype of respected sowing seed selected for the prediction of yield value. Moreover, while combining the statistical data and image data for the prediction, the generalization ability of prediction model was affected by reason of the images that shared the same timestamp as the statistical data were eliminated as part of the procedure for creating the dataset utilized in the existing approaches. Hence, a novel, Yield Environ Netsual Network (YENN) has been proposed which is consists of two deep networks; (i) Deep Q network (DQN) and (ii) VGG16 for the generalization ability as well as the elimination of data caused by the same timestamp is rectified. Here, VGG-16 is utilized for processing the given input images. As a result, the proposed model well examine the potential disease based on the gene and environment conditions and effectively predict the yield value of crops.

Artificial intelligence applied to estimate soybean yield

The application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.

Yield estimation of radionuclides from 6,7Li-induced reactions: A comparative analysis for 97,95Ru

The article reports the production yields of the medically relevant Ru radionuclides and other co-produced radionuclides from 6,7Li-induced reactions on 93Nb target within the 20–45 MeV energy range. The residues were measured employing the activation technique followed by the offline γ-spectroscopy. Statistical model calculations using EMPIRE3.2.2 code are employed to assess the optimized nuclear model parameters and production mechanisms of the residues. As an outcome, new data from 6Li reaction suggests 9720 MBq/C of thick target yield (TTY) for the production of 95Ru with minimal impurities. While 7Li reaction may be relied upon for producing 97Ru, yielding 813 MBq/C TTY within the studied energy range.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

Tea yield estimation using UAV images and deep learning

Traditional estimation of tea yield significantly depends on fields observation, which is facing operational and management challenges due to increasing farm sizes and rising labor costs. Development of artificial intelligence such as deep learning (DL) and unmanned aerial vehicles (UAVs) provide opportunities where intelligent system of tea fields will be constructed. This study carried out the application of tea yield estimation model to practical field scenarios. A large dataset of UAV tea buds’ images was built, which contained 5899 images and 29,958 labelled tea buds. Based on the large dataset, YOLOv5 model was used to train, and CSPDarknet53 performed best over Swin Transformer and ConvNeXt as backbone. The mean average precision (mAP), precision, recall and F1 score of CSPDarknet53 were respectively 85.63%, 84.91%,72.19% and 78.00%. Meanwhile, the large database weakened the differences among models of different sizes. Squeeze-and-excitation (SE) block was inserted, and the mAP of all models with CSPDarknet53 and SE was over 85.00%, indicating models could be applied to tea fields. To reduce the number of repeated tea bud detection boxes in the prediction process, a second non-maximum suppression (NMS) filter was used with threshold value of 0.20. The yield of fresh tea leaves before and after picking was estimated according to the number of tea buds detected in different seasons and different types of tea plants. The yield of fresh tea leaves in spring before picking was estimated to 1223.22 kg/ha. This study is a complete report from model training to tea field application, and will advance the industrialization development of agriculture.

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

ESTIMATION OF SUGARCANE YIELD USING MULTI-TEMPORAL SENTINEL 2 SATELLITE IMAGERY AND RANDOM FOREST REGRESSION

Abstract. Advancements in remote sensing techniques have greatly enhanced crop monitoring and yield estimation, with spectral vegetation indices (VIs) serving as a key component. Our study investigates the use of Sentinel-2 data, notable for its red-edge bands, in estimating sugarcane yield in Ethiopia's Awash Basin. Utilizing 22 VIs from S2 imagery, our approach combines Random Forest (RF) regression with the Recursive Feature Elimination (RFE) algorithm to improve the accuracy of sugarcane yield predictions. The results demonstrate the superior performance of the RF-RFE method over traditional RF with full datasets and Stepwise Multiple Regression (SMR). Particularly, VIs focusing on the red-edge spectral bands of S2 - such as NDVIre1n, NDVIre2n, NDVIre3n, NDRE1 and NDRE2 - were crucial in enhancing prediction precision. These indices from the red-edge and NIR narrow bands consistently influenced yield estimations in both the Wonji-Shoa and Metehara estates. The study underscores the critical role of the RFE algorithm in optimal variable selection, reinforcing earlier findings that precise variable choice can substantially boost model accuracy. The enhanced performance of the RF model when paired with the RFE algorithm was evident, emphasizing the importance of variable selection in accurate yield predictions. Employing the Out-of-Bag RMSE (OOB_RMSE) error estimate for evaluation, we observed variations in OOB_RMSE performance with different RF parameters, identifying the ntree value of 500 as optimal for the studied regions. The RF-RFE model’s estimations showed lower errors and higher correlation coefficients, proving its efficacy over a full dataset approach, which faced challenges with traditional VIs' saturation. Our findings align with earlier studies, highlighting the efficiency of S2’s red-edge bands in diverse estimation tasks and a shift towards using freely available broadband images like S2, over hyperspectral imagery, due to reduced data redundancy and processing costs. In conclusion, our findings reveal that RF regression, particularly when integrated with the RFE algorithm, is a powerful tool in remote sensing applications. The S2 imagery is optimal VIs, predominantly from the red-edge bands, exhibit significant potential for sugarcane yield estimations. The impressive results of the RF-RFE method, evident in metrics like MAE, MAPE, RMSE, Mean percentage, and R2, advocate its invaluable role in sugarcane yield prediction, highlighting its potential for optimizing irrigation management strategies and broad-spectrum agricultural planning.

Specific detection of sarin gas Mimicking, diethylchlorophosphate employing solvent mediated in-situ generated hydroxyl functional group in a phthalimide-chromone coupled fluorogenic probe

Nerve agents are particularly hazardous substances and can result in fatality due to suppressing acetylcholinesterase function. This article introduced a fluorescent probe, (E)-5-(((4-hydroxy-2-methoxy-2H-chromen-3-yl)methylene)amino)isoindoline-1,3-dione (CMAD), which was exceptionally effective at detecting the sarin replacement diethylchlorophosphate (DCP), both in solution and vapor phase, respectively which exhibited quick response, excellent sensitivity and specificity. As a result of the gradual addition of DCP into the solution of the probe, the photoluminescence intensity at 476 nm is markedly increased, exhibiting bright cyan color fluorescence because of inhibition of the excited state intramolecular proton transfer (ESIPT) process, which is also clearly reflected in the estimation of photoluminescence quantum yield of phosphorylated CMAD (0.55 relatives to 4-aminophalimide) and the color chromaticity (CIE) diagram. The lowest detection limit and limit of quantification are 3.25 µM and 10.8 µM, respectively, within the stockpiles of other analogous analytes. Additionally, for on-the-spot detection, the probe may also be utilized in the test strips with superior selectivity for DCP. The ability to identify and measure DCP in the gas phase has also been established using a dip-stick and dipped-vial-conical-flask analysis. Moreover, we also performed the smartphone-based technique for the practical utility of CMAD for the on-spot quantification and detection of sarin gas in real threat situations. The effective demonstrations of practical usage, including soil analysis, inspire additional applications in different fields. Therefore, probe CMAD is a reliable, on-site monitoring fluorescence probe for precisely identifying DCP in the presence of additional competing nerve agents.

Improving soybean yield prediction by integrating UAV nadir and cross-circling oblique imaging

High-throughput estimation of soybean yield using unmanned aerial vehicle (UAV) imagery can help improve the efficiency of soybean breeding. Previous studies have mainly focused on the extraction of vegetation indices and texture features from two-dimensional(2D) orthophotos to construct empirical models of yield, lacking spatial structure information of crops. Therefore, UAV cross-circling oblique (CCO) photography combined with SfM-MVS algorithm was used to reconstruct three-dimensional(3D) soybean canopy structure. Then canopy 3D related phenotypic features are extracted and combined with features from RGB nadir and multispectral images to analyze the capability of different modal data fusion on soybean yield prediction. In addition, Shapley value was used to evaluate the importance of features across different machine learning models. Based on the Shapley value, a bagging-stacking ensemble learning framework was developed using Lasso, Random Forest (RF), Ridge Regression (RR), and XGBoost as base learners for yield prediction. The performance of the traditional stacking method was evaluated and compared with weighted average methods such as Bayesian Model Averaging (BMA) and Entropy Weighted Average (EWA) as meta-learners in the ensemble framework. The results show that 3D canopy structure of soybean can be obtained from UAV CCO photography. The inclusion of 3D structural features can improve the accuracy of yield estimation. Among different modal data combinations, the highest estimation accuracy was achieved when combining RGB nadir features with CCO 3D features. The performance of the above base learners was improved by 8.8%, 3.5%, 7.1%, and 8.0% respectively when using the Shapley value method. The accuracy of yield prediction applied on independent dataset of year 2023 was further calculated by using the bagging-stacking ensemble learning method. When using BMA as the meta-learner, the best performance is obtained with an R2 of 0.7. Therefore, UAV CCO photography with SfM-MVS algorithm provides a new approach to obtain high-quality point clouds of the crop canopy at low cost. UAV multimodal data combined with ensemble learning models allow accurate estimation of yield prediction in breeding materials of soybeans at the plot scale.

Segmentation and yield count of an arecanut bunch using deep learning techniques

Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.

Acoustic source characterization for chemical explosions in air

Chemical explosions in the air generate large pressure disturbances. These pressure waves propagate as non-linear shock waves near the source and transition into acoustic waves in the far-field. Since low-frequency acoustic waves propagate long distances without significant loss of energy, acoustic signals induced by explosions are often used to determine explosion energies of the events in terms of an explosion yield. In order to estimate the explosion energy accurately, the relationship between acoustic energy and explosion yield must be understood. However, explosion yields are often measured by non-linear shock waves in the near-field, and it is not clear how much acoustic energy accounts for the explosion energy. In addition, acoustic signals typically have lower-frequency contents than shock waves in the near-field, and hence frequency-dependent explosion energy should be understood to accurately infer explosion yields based on acoustic observations. In this study, we investigate the relationship between acoustic energy and explosion yield based on ground-truth explosion data. A standard acoustic source waveform will be determined by acoustic observations, and frequency-dependent energy will be explored for yield estimation. We will demonstrate that this frequency-dependent acoustic source characterization can improve the accuracy and confidence of explosion yield estimation.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest: A Case Study in Henan Province, China

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest: A Case Study in Henan Province, China

A novel microwave Doppler method for mass estimation of machine-picked seed cotton

Obtaining information on cotton yields and creating a spatial map of cotton production is the starting point for implementing precision agriculture. Microwave technology has been used for yield estimation in China for crops such as corn and soybeans and has shown strong applicability. However, research on cotton yield estimation using microwave technology is weak. Therefore, this paper proposes a method for seed cotton mass estimation based on the microwave Doppler method and designs bench tests to prove the feasibility of the method. First, a seed cotton speed estimation model was established based on the microwave Doppler effect and fast Fourier transform. On this basis, a seed cotton velocity estimation experiment was designed to study the effects of sensor installation angle and position, pipe collision friction, air resistance, and the interaction force between the seed cotton on the seed cotton movement velocity. The power spectral density (PSD) of the acquired microwave data is then estimated using the modified periodogram method. The least squares method was used to establish a linear regression model between seed cotton mass and the corresponding PSD, and finally the free-fall mass experiment and pneumatic conveying mass experiment yielded that the estimation errors of seed cotton mass were 7.30% and 6.17%, respectively. The experimental results show that the estimation error is small when using microwave technology for seed cotton mass estimation, which meets the requirements of actual operation.

Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network

This paper, "Weather and Crop Yield Prediction by Machine Learning Model and Spiking Neural Network," is to improve crop yield prediction efficiency by utilizing cutting-edge technology and transform agricultural practices. The creation of two prediction models specifically for this purpose is the main goal of the study. The first model incorporates geographical information such as Normalized difference vegetation index (NDVI), Standard Precipitation Index (SPI), and Vector Choquet Integral (VCI) and uses a Genetic Algorithm to reduce the size of the input dataset. This reduction attempts to lessen learning process confusion brought on by comparable data with different values. The model improves its capacity to identify pertinent patterns for precise yield estimates by incorporating spatial data. The SoftMax function is employed by the second model, a Modified Convolutional Neural Network (MCNN), as an error correcting method. By reducing errors and raising the overall accuracy of the model, the SoftMax function improves the output and produces forecasts that are more accurate. The study emphasizes how crucial geographic data including satellite imagery is for supplying crucial insights for predicting crop yields. Farmers can make well-informed decisions by having a thorough understanding of agricultural dynamics, which is facilitated by spatial patterns and temporal shifts. Statistical and geographic data are used to completely study the effects of numerous variables, such as biological, economic, and environmental ones, on crop yield production. The goal of the project is to create precise and effective instruments that enable farmers to make knowledgeable decisions about their soil by combining machine learning with spiking neural networks.

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data.

Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning

A rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia Oleifera forests. Consequently, the simultaneous and accurate detection of key phenology features of Camellia Oleifera (buds, flowers, fruits) is crucial for precise yield estimation. This not only enables robotic harvesting but also allows for the prediction of peak flowering and fruit ripening periods to determine the optimal harvesting time. However, in recent studies, only Camellia Oleifera fruits have been marked and detected. Therefore, to enable rapid yield estimation, it is necessary to simultaneously detect the key phenology stages (buds, flowers, fruits) of Camellia Oleifera. In this study, we annotated, trained, and predicted Camellia Oleifera buds, flowers, and fruits collected via field cameras to observe their daily quantitative changes. Quantity change curves were generated to estimate crucial phenology stages. Phenology feature detection and transfer learning were performed using the YOLO v5 model, widely used YOLO v3 model, and CenterNet model with center point prediction, all trained on the same dataset. The best model for phenology feature detection was selected based on a comparison of average precision, with the YOLO v5 model achieving a higher mean Average Precision (mAP) value of 91.31 % compared to the CenterNet (85.43 %) and YOLO v3 (81.00 %) models. In YOLO v5, the AP values for bud, flower, and fruit detection were 82.80 %, 98.13 %, and 92.99 %, respectively, surpassing the CenterNet model by 3.97 %, 2.44 %, and 11.23 %, and the YOLO v3 model by 6.39 %, 17.13 %, and 11.67 %. The image size was adapted from 4000 × 3000 pixels to 512 × 512 pixels for model optimization. Additionally, data from the Seedling Center of Liuyang City collected at different years and times were utilized to showcase the generalizability and scalability of the selected models, resulting in mAP values of 86.14 %, 80.17 %, and 69.20 % for the three above-mentioned models respectively. The plotted phenology change curves unveiled that Camellia Oleifera undergoes four stages: fruit enlargement period, bud enlargement period, flowering period, and flower wilting period. The conclusion can be drawn that using field cameras and YOLOv5 can simultaneously achieve real-time detection of key phenology features (buds, flowers, and fruits) of Camellia Oleifera, in order to further record crucial phenology patterns (such as flowering peaks and fruit ripening periods). This study offers theoretical references and scientific evidence for monitoring changes in key phenology features of Camellia Oleifera.

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI’s over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a stalk density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal stalk density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha−1, slightly higher than the sampling yield. The root mean square error of the estimation was 6.63 Mg ha−1, which was 5.18% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale.