Image processing and impact analyses of terminal heat stress on yield of lentil.

Abstract

Abiotic factors, including heat stress, significantly impact the growth and development of lentil across the globe. Although these stresses impact the plant's phenotypic, genotypic, metabolic, and yield development, predicting those traits in lentil is challenging. This study aimed to construct a machine learning-based yield prediction model for lentil using various yield attributes under two different sowing conditions. Twelve genotypes were planted in open-field conditions, and images were captured 45 days after sowing (DAS) and 60 DAS to make predictions for agro-morphological traits with the assessment for the influence of high-temperature stress on lentil growth. Greening techniques like Excess Green, Modified Excess Green (ME × G), and Color Index of Plant Extraction (CIVE) were used to extract 35 vegetative indices from the crop image. Random forest (RF) regression and artificial neural network (ANN) models were developed for both the normal-sown and late-sown lentils. The ME × G-CIVE method with Otsu's thresholding provided superior performance in image segmentation, while the RF model showed the highest level of model generalization. This study demonstrated that yield per plant and number of pods per plant were the most significant attributes for early prediction of lentil production in both conditions using the RF models. After harvesting, various yield parameters of the selected genotypes were measured, showing significant reductions in most traits for the late-sown plants. Heat-tolerant genotypes like RLG-05, Kota Masoor-1, and Kota Masoor-2 depicted decreased yield and harvest index (HI) reduction than the heat-sensitive HUL-57. These findings warrant further study to correlate the data with more stress-modulating attributes. Supplementary information The online version contains supplementary material available at 10.1007/s13205-024-04031-5.

Plant phenotyping relevance

画像から植生指標を抽出し、画像分割と機械学習によってレンティルの形態・収量形質を予測する手法の構築と評価が研究の中心である。

Code and data availability

The supplied blocks describe the authors' lentil RGB imagery, RF/ANN models, and trait predictions, but contain no public deposit of phenotype data, images, code, or trained models. The only availability statement is a generic supplementary-material link with no stated contents; all other URLs are cited references.

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