Crop Yield Estimation Research Articles

CETR: CenterNet-Vision transformer model for wheat head detection

<p>Wheat head detection is a critical task in precision agriculture for estimating crop yield and optimizing agricultural practices. Conventional object detection architectures often struggle with detecting densely packed and overlapping wheat heads in complex agricultural field images. To address this challenge, a novel CEnternet-vision TRansformer model for Wheat Head Detection (CETR) is proposed. CETR model combines the strengths of two cutting-edge technologies—CenterNet and Vision Transformer. A dataset of agricultural farm images labeled with precise wheat head annotations is used to train and evaluate the CETR model. Comprehensive experiments were conducted to compare CETR’s performance against convolutional neural network model commonly used in agricultural applications. The higher mAP value of 0.8318 for CETR compared against AlexNet, VGG19, ResNet152 and MobileNet indicates that the CETR model is more effective in detecting wheat heads in agricultural images. It achieves a higher precision in predicting bounding boxes that align well with the ground truth, resulting in more accurate and reliable wheat head detection. The higher performance of CETR can be attributed to the combination of CenterNet and ViT as a two-stage architecture taking advantage of both methods. Moreover, the transformer-based architecture of CETR enables better generalization across different agricultural environments, making it a suitable solution for automated agricultural applications.</p>

Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework’s approach involves base tasks using LightGBM and Bayesian Optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive R2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method’s significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data

Timely and accurate extraction of crop planting units plays a critical role in crop yield estimation, soil management, food supplies, and disaster warnings. However, precisely mapping crop types is challenging in smallholder farming systems due to heterogeneous and mixed pixels, where field sizes are small, and crop types are very diverse. In this paper, the crop type system of Huocheng County in northwest China as an example area, crop classification feature variables are constructed using Sentinel-2 remote sensing images, and combining the ReliefF algorithm, three feature selection classification models are established by 3092 crop type sampling points data. The crop planting units are extracted using pixel-based and object-based classification methods, respectively, and unmanned aerial vehicle (UAV) data assess the accuracy as ground truth. The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. This is particularly evident when integrating this approach into pixel-based classification, addressing challenges such as low pixel classification accuracy in regions of this type. The validation results from UAV data also confirm these findings. At validation points 2, 3, and 4, pixel-based classification demonstrates significantly higher accuracy compared to object-based classification, with pixel-based classification accuracy improving by 9.16%, 7.83%, and 31.83%, respectively. Although the UAV validation accuracy does not reach a high level, pixel-based classification remains the optimal choice for smallholder crop classification. This method offers new insights and references for research related to the classification of smallholder crops with complex planting structures.

High Spatial Resolution Soil Moisture Improves Crop Yield Estimation

High Spatial Resolution Soil Moisture Improves Crop Yield Estimation

Assimilation of Earth Observation Data for Crop Yield Estimation in Smallholder Agricultural Systems

Assimilation of Earth Observation Data for Crop Yield Estimation in Smallholder Agricultural Systems

Transformative crop insurance solution with big earth data: Implementation for potato in India

ContextCrop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts. ObjectiveThis paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices. MethodSatellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information measurement that produces balanced relationships and unbiased weights. The CHF was first generated with data from the past years (2016–2019), and the resulting weights were then applied to the normalized data of the current year (2020). ResultsThe current crop insurance scheme, using CHF data instead of yield data, was implemented in the state of West Bengal, India, covering about 500,000 ha of potato across and one thousand insurance units in the 2020 crop season. The CHF and yield data from past years showed similar patterns in the majority of cases. The indemnity level was set at 70 % of the normal CHF, which was the average CHF of past years. Loss assessment and compensation payouts for the current year were determined by the extent of CHF reduction beyond the indemnity level. SignificanceThis new index-insurance scheme has many advantages over the conventional yield-based scheme in terms of transparency, objectivity and ease of implementation. There is scope for improving the composite index with additional features. Such technology-driven index-insurance schemes for field crops are expected to bring a paradigm shift in the crop insurance sector, giving rise to new business models and benefitting all the stakeholders.

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by 'Carbon Peak' target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future.

Estimation of plant's morphological parameters using terrestrial laser scanning-based three-dimensional point cloud data

The yield of crop is very important for sustainable agriculture to meet continuously growing demand of food. The morphological parameters, such as plant height (PH), projected leaf area (PLA), and plant volume (PV) are important metrics to estimate crop yield. In this paper, a multi-step methodology is proposed to estimate parameters PH, PLA and PLA using state-of-the-art three-dimensional (3D) mapping technique: terrestrial laser scanning (TLS). The proposed methodology consists of three well-designed steps: pre-processing, ground filtering and clustering, and estimation of plant's morphological parameters. In pre-processing TLS-derived laser scans acquired from multiple scan stations to cover the selected agricultural test site registered and coordinate transformation is performed to convert the TLS point cloud into total station (TS)-based locally defined coordinate system. The second step transformed point cloud data is processed to generate ground and non-ground points, where subsequently Euclidean clustering is employed to generate clusters of non-ground points. The clustered data is cleaned based on geometrical constraints and cabbage plant's clusters are extracted, which are processed using height-based analysis and convex hull algorithm to estimate PH, PLA and PLA of each cluster. The proposed methodology performance was evaluated using TLS point cloud data of selected agricultural field of cabbage plants, and PH, PLA and PLA estimation was performed at RMSE of 0.005 m, 0.012 m2 and 0.0063 m3 respectively. The proposed methodology is efficient and easy to implement step-wise approach from data collection to processing for plant's geometrical analysis, thus it has potential to be used as one of the industrial tools for precision agriculture.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.

The Fragile State of Industrial Agriculture: Estimating Crop Yield Reductions in a Global Catastrophic Infrastructure Loss Scenario.

Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large-scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber-attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop-specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%-48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system.

Winter wheat yield estimation at the field scale using sentinel-2 data and deep learning

Accurately expressing the spatial pattern of crop yield at a sizeable regional field scale is paramount for precision agriculture. However, the current methodologies consistently face numerous challenges, comprising inadequate yield samples and algorithmic tendencies to underestimate higher yield values and overestimate lower ones. Data-driven deep learning algorithms effectively transform remotely sensed data into high-dimensional feature representations. Developing a cost-efficient model that minimizes the impact of insufficient field samples while maximizing the representation of crop yield differences on plots is worthwhile. The study developed an ACNN (Attention-based One-dimensional Convolutional Neural Network) model to efficiently extract and optimize the spatiotemporal features of winter wheat yield from Sentinel-2 MSI Level-2A data while relatively reducing the model's reliance on training samples. Winter wheat yield at both county and field scales in Henan Province was estimated utilizing Random Forest (RF), Convolutional Neural Network (CNN), and the ACNN models. The proposed ACNN model effectively identified key phenological stages and discerned noteworthy spectral bands essential for field-scale winter wheat yield estimation. It underscores the heading stage as a pivotal period influencing yield formation, emphasizing the indispensable contributions of the B8 (835.1 nm) and B12 (2202.4 nm) spectral bands in yield estimation. Moreover, this model adeptly overcame the challenge of underestimating higher values and overestimating lower values in crop yield estimation while showcasing robust generalization ability. The study presents a practical method for field-scale crop yield estimation over a large area with insufficient yield samples.

Deep Learning based flower detection and counting in highly populated images: A peach grove case study

Farmers and producers need to estimate crop yield in order to plan and allocate human and economic resources during the harvesting season. For many crops, such as peach groves, the number of fruits is correlated with the number of flowers produced by each tree. Therefore, estimating the number of flowers in peach groves can serve as a good indicator of crop yield, disregarding climate hazards. However, in peach groves, tree images present several challenges, including a high number of flowers, interference from distant trees, and occlusion between elements. These issues pose a difficult task for computer vision and machine learning techniques. In this study, we propose the utilization of state-of-the-art deep learning techniques for image detection purposes; namely the YOLO architectures on its versions 5, 7, and 8 and their different size models (n, s, m, l, x); as well as predicting object density using multi-column in densely populated images, using a multi-column deep neural network. The methodology was tested on a new dataset comprising 600 images of peach trees during the blooming season, in the region of Catalonia, Spain. Out of these, 400 images were used to train the model, while 100 were allocated for testing and another 100 for validation. The counting results were evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and percentage error (%Err). For the detection algorithms, metrics such as accuracy, precision, recall, and mean average precision were utilized, alongside metrics for evaluating the counting process.The experiments demonstrated that predicting the density map yielded better results in the counting process, achieving an MAE of 39.13, RMSE of 69.69, and a percentage error of 9.98. The detection algorithm that exhibited superior performance was YOLOv7x, with metrics of MAE 152.7, RMSE 212.9, and a percentage error of 29.7 %. These results indicate that, for counting purposes, predicting the density map produced better overall outcomes.

3D Convolutional Neural Network with Dimension Reduction and Metric Learning for Crop Yield Prediction Based on Remote Sensing Data

Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received increasing attention. Information mining from multichannel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multispatial images by using the dimension reduction method and a 3D convolutional neural network. In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. We evaluated the proposed method on county-level soybean yield prediction in the United States, and the experimental results show the effectiveness of the proposed method. The proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation.

Automated lag-phase detection in wine grapes using a mobile vision system

Lag-phase in wine grapes refers to a critical stage in the growth cycle of berries when there is a significant slowdown in their growth rate roughly at half the harvest size. This stage is essential for predicting the crop yield at harvest as there is a strong relationship between the lag-phase berry size and the final berry size in grape clusters. This study is focused on developing an automated system to detect and predict the onset of the lag-phase in grape berries using a mobile machine vision technique. The system utilized various image processing techniques, including deep learning algorithms, to detect berries from grape clusters and estimate their size (diameter). The sizes of sample berries in randomly selected grape clusters were tracked and their growth patterns throughout the growing season were analyzed to identify the onset of the lag-phase. Berry size estimated using a Mask R-CNN-based segmentation and checkerboard calibration object achieved an RMSE of 0.473 mm and an R2 of 0.837 against the ground truth measurements. The mean absolute error (MAE) for detecting the lag-phase start date was estimated to be seven days. The results showed that the automated method was accurate and effective in detecting the onset of lag-phase. Overall, the study shows promise for using a deep learning-based technique to automate lag-phase detection and estimate crop yield, which can help growers make more informed decisions on crop management, inform harvest and post-harvest logistics, and improve the overall efficiency of farming operations.

TOWARDS WHEAT YIELD ESTIMATION IN PLANT BREEDING FROM INHOMOGENEOUS LIDAR POINT CLOUDS USING STOCHASTIC FEATURES

Abstract. The world relies heavily on wheat, corn, and rice for nutrition, with global challenges such as population growth and climate change threatening food security. To tackle this, plant breeding, supported by digital technologies, focuses on improving food quality and quantity. Currently, crop yield estimation uses indirect observations through hyperspectral data and spectral indices, such as NDVI, which suffer from low sensitivity in breeding scenarios. Terrestrial laser scanners (TLS) present an alternative, allowing observations of the quantity and morphology of wheat ears from point clouds, which are directly linked to grain yield. However, exploiting these observations under field conditions presents challenges, mainly due to reduced resolution and non-homogenous properties of point clouds. In response, we propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds that are specifically handcrafted to be less sensitive to the abovementioned phenomena. This approach avoids the need for explicit 3D reconstruction of individual plants and plant organs. Our initial results show limited success in yield estimation when posed as a regression problem. However, when framed as a classification problem focusing on detecting top- and bottom-performing plant phenotypes, we achieved a promising accuracy of 84.4% and AUC of 0.93. While encouraging, these are only the first results under relaxed conditions and further work is needed to enhance practical applicability.

The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations

Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Early crop yield prediction for agricultural drought monitoring using drought indices, remote sensing, and machine learning techniques

Abstract The unpredictability of crop yield due to severe weather events such as drought and extreme heat continues to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (MLR), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.

Winter Wheat Yield Estimation Based on Multi-Temporal and Multi-Sensor Remote Sensing Data Fusion

Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R2 of 0.83, an RMSE of 297.70 kg/hm2, and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R2 of 0.80 and 0.80, RMSE of 330.37 kg/hm2 and 328.95 kg/hm2, and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation.

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R2 > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models.

Mapping crop yield spatial variability using Sentinel-2 vegetation indices in Ethiopia

Crop yield prediction before harvest is a key issue in managing agricultural policies and making the best decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their agricultural targets. However, crop yield estimates rely on labor-intensive surveys in Ethiopia. To solve this, we used Sentinel-2, crop canopy analyzer, and ground-truthing data to estimate grain yield (GY) and aboveground biomass (AGB) of two major crops, teff and finger millet, in 2020 and 2021 in Ethiopia’s Aba Gerima catchment. We performed a supervised classification of October Sentinel-2 images at the tillering stage. Among vegetation indices and leaf area index (LAI) used to predict teff and finger millet GY and AGB, the enhanced vegetation index (EVI) and normalized-difference VI (NDVI) provided the best fit to the data. NDVI and EVI most influenced teff AGB (R2 = 0.87; RMSE = 0.50 ton/ha) and GY (R2 = 0.84; RMSE = 0.14 ton/ha), and NDVI most influenced finger millet AGB (R2 = 0.87; RMSE = 0.98 ton/ha) and GY (R2 = 0.87; RMSE = 0.22 ton/ha). We found a close association between GY and AGB and the satellite EVI and NDVI. This demonstrates that satellite images can be employed in yield prediction studies. Our results show that satellite and crop canopy analyzer-based monitoring can facilitate the management of teff and finger millet to achieve high yields and more sustainable food production and environmental quality in the area. The results could be reproducible under similar study catchment conditions and boost crop yield. Extrapolation of the models to other areas requires local validation. To improve crop monitoring for farmers and reduce expenses, we suggest integrating time series Sentinel-2 images along with LAI obtained from crop canopy analyzers collected during the cropping season.