Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

Ribeiro, João Everthon da Silva; Coêlho, Ester dos Santos; Oliveira, Anna Kézia Soares de; Silva, Antonio Gideilson Correia da; Lopes, Welder de Araújo Rangel; Oliveira, Pablo Henrique de Almeida; Silva, Elania Freire da; Júnior, Aurélio Paes Barros; Silveira, Lindomar María da

doi:10.1016/j.heliyon.2023.e17834

Cited by 2 publications

(1 citation statement)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings distinctly indicate the superior accuracy of the ANN model over the image processing method, as shown in Figure 10. Studies [64,65] utilized ANNs with architectures like 2-50-1 and 2-3-1, achieving accuracy rates of 99.99% and high correlation (>0.98) for estimating leaf area in various plant species, including wheat, triticale, durum, and sesame. Another study [66] compared methods like ANN, adaptive neurofuzzy inference system, and regression, reporting accuracy ranges of 97-99% for cereals.…”

Section: Leaf Area Estimation Using Ann Modelmentioning

confidence: 99%

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

View full text Add to dashboard Cite

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

show abstract

Section: Leaf Area Estimation Using Ann Modelmentioning

confidence: 99%

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

View full text Add to dashboard Cite

show abstract

Potential of Thermal and RGB Imaging Combined with Artificial Neural Networks for Assessing Salt Tolerance of Wheat Genotypes Grown in Real-Field Conditions

El-Hendawy,

Tahir,

Al-Suhaibani

et al. 2024

Agronomy

View full text Add to dashboard Cite

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R2 values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R2 values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R2 values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs.

show abstract

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

Cited by 2 publications

References 68 publications

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Potential of Thermal and RGB Imaging Combined with Artificial Neural Networks for Assessing Salt Tolerance of Wheat Genotypes Grown in Real-Field Conditions

Contact Info

Product

Resources

About