Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network

Abstract

IntroductionEstimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level.MethodsThis study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the ‘Independence’ cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level.ResultsThe deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation.DiscussionThis study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects

“…The study demonstrated a strong seasonal correlation between CWSI and final yield (R 2 = 0.80) using a non-water stress baseline (NWSB) established over three years. Tang et al [116] explored RS technologies for yield estimation in almond tree crops at the field scale. Traditional and ML methods, including Random Forest Regression (RFR), Gradient Boosting Trees for Regression (GBTR), and XGBoost models, were developed, incorporating Landsat VIs and weather data.…”

Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation

“…Finally, through feature priority and model optimization, the R 2 of the optimal model on the validation set reached 0.813, and on the test set this reached 0.758, providing a good case for appleyield prediction based on remote sensing images. Tang et al [82] collected multispectral aerial images of almonds and established an improved CNN network for almond-yield prediction, achieving good prediction accuracy. The results were significantly better than those obtained by machine learning algorithms based on vegetation indices, demonstrating the advantages of deep learning algorithms in automatically extracting features.…”

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

“…NIR is used especially for highlighting the leaves and the production of almonds in a tree. For example, in ( Tang et al., 2023 ) aerial multi-spectral images (near-infrared, red edge, red, and green) are processed by a CNN to estimate the almond production in an orchard with a coefficient of determination, R2 = 96%. It is specified that the sun-shadow effect can decrease system performance.…”