Crop Yield Estimation through Remote Sensing Data

Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R²) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R² = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R² = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R² = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R² and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data. Full article

Currently, estimations of strawberry productivity are conducted manually, which is a laborious and subjective process. The use of more efficient and precise estimation methods would result in better crop management. The objective of this study was to assess the performance of two regression algorithms-Linear Regression and Support Vector Machine—in estimating the average weight and number of fruits and the number of leaves on strawberry plants, using multispectral images obtained by a remotely piloted aircraft (RPA). The experiment, which was conducted in the experimental area of the Botany Laboratory at the Federal University of Uberlândia-Monte Carmelo Campus (Universidade Federal de Uberlândia, Campus Monte Carmelo), was carried out using a randomized block design with six treatments and four replications. The treatments comprised six commercial strawberry varieties: San Andreas, Albion, PR, Festival, Oso Grande, and Guarani. Images were acquired on a weekly basis and then preprocessed to extract radiometric values for each plant in the experimental area. These values were then used to train the production prediction algorithms. During the same period, data on the average fruit weight, number of fruits per plant, and number of leaves were collected. The total fruit weight in the field was 48.08 kg, while the linear regression (LR) and Support Vector Machine (SVM) estimates were 48.04 and 43.09 kg, respectively. The number of fruits obtained in the field was 4585, and the number estimated by LR and SVM algorithms was 4564 and 3863, respectively. The number of leaves obtained in the field was 10,366, and LR and SVM estimated 10,360 and 10,171, respectively. It was concluded that LR and SVM can estimate strawberry production and the number of fruits and leaves using multispectral unmanned aerial vehicle (UAV) images. The LR algorithm was the most efficient in estimating production, with 99.91% accuracy for average fruit weight, 99.55% for the number of fruits and 99.94% for the number of leaves. SVM exhibited 89.62% accuracy for average fruit weight, 84.26% for the number of fruits, and 98.12% for the number of leaves. Full article

Crop growth and yield monitoring are essential for food security and agricultural economic return prediction. Remote sensing is an efficient technique for measuring growing season crop canopies and providing information on the spatial variability of crop yields. In this study, ten vegetation indices (VIs) derived from time series PlanetScope and Sentinel-2 images were used to investigate the potential to estimate corn grain yield with different regression methods. A field-scale spatial crop yield prediction model was developed and used to produce yield maps depicting spatial variability in the field. Results from this study clearly showed that high-resolution PlanetScope satellite data could be used to detect the corn yield variability at field level, which could explain 15% more variability than Sentinel-2A data at the same spatial resolution of 10 m. Comparison of the model performance and variable importance measure between models illustrated satisfactory results for assessing corn productivity with VIs. The green chlorophyll vegetation index (GCVI) values consistently produced the highest correlations with corn yield, accounting for 72% of the observed spatial variation in corn yield. More reliable quantitative yield estimation could be made using a multi-linear stepwise regression (MSR) method with multiple VIs. Good agreement between observed and predicted yield was achieved with the coefficient of determination value being 0.81 at 86 days after seeding. The results would help farmers and decision-makers generate predicted yield maps, identify crop yield variability, and make further crop management practices timely. Full article

In-season sensing can account for field variability and improve nitrogen (N) management; however, opportunities exist for refinement. The purpose of this study was to compare different sensors and vegetation indices (VIs) (normalized difference vegetation index (NDVI); normalized difference red edge (NDRE); Simplified Canopy Chlorophyll Content Index (SCCCI)) at various corn stages to predict in-season yield potential. Additionally, different methods of yield prediction were evaluated where the final yield was regressed against raw or % reflectance VIs, relative VIs, and in-season yield estimates (INSEY, VI divided by growing degree days). Field experiments at eight-site years were established in Mississippi. Crop reflectance data were collected using an at-leaf SPAD sensor, two proximal sensors: GreenSeeker and Crop Circle, and a small unmanned aerial system (sUAS) equipped with a MicaSense sensor. Overall, relative VI measurements were superior for grain yield prediction. MicaSense best predicted yield at the VT-R1 stages (R² = 0.78–0.83), Crop Circle and SPAD at VT (R² = 0.57 and 0.49), and GreenSeeker at V10 (R² = 0.52). When VIs were compared, SCCCI (R² = 0.40–0.49) outperformed other VIs in terms of yield prediction. Overall, the best grain yield prediction was achieved using the MicaSense-derived SCCCI at the VT-R1 growth stages. Full article