TURION: A physiological crop model for yield prediction of asparagus using sentinel-1 data

Romero-Vergel, Angela  Patricia

doi:10.1016/j.eja.2022.126690

Cited by 3 publications

(2 citation statements)

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“…Thus, knowledge of the level of CHO in the roots at distinct times during the seasonal growth of asparagus becomes a useful measure for assessing the condition of the crop. Researchers and growers around the world have been using various measures of root CHO to quantify asparagus productivity ( Wilson et al., 2008 ; Paschold et al., 2008a ; Romero-Vergel, 2023 ).…”

Section: Environmental Issuesmentioning

confidence: 99%

Asparagus breeding: Future research needs for sustainable production

Drost

2023

Front. Plant Sci.

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Productivity in asparagus (Asparagus officinalis L.) is determined in part by (1) the selection of superior, adaptive genetics, (2) matching the selected genetics to the production environment, and (3) managing the crop production system in ways to maximize harvest potential that are sustainable, profitable, and efficient. Over the last 100 years, a considerable effort by asparagus researchers has gone into breeding superior genetic lines, testing those in numerous locations, and studying how asparagus responds to a multitude of inputs (fertilizers, irrigation, fungicides, herbicides, insecticides). Farmers worldwide have benefited from all of these improvements. However, as we look to the future, we need to change our research approaches to deal with widely accepted limitations to asparagus growth that if left unanswered will further erode the long-term sustainability and profitability of the crop. In addition, there is a growing need for increased mechanization to offset labor needs. To effectively harvest asparagus, new plant types with more predictable spear emergence patterns need to be bred. This paper will briefly review the historic content of asparagus research and open a discussion on how to refocus international research efforts to breed superior plant materials to meet the challenges of the future.

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Section: Environmental Issuesmentioning

confidence: 99%

Asparagus breeding: Future research needs for sustainable production

Drost

2023

Front. Plant Sci.

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“…Various studies have focused on specific crops, regions, and seasons to tailor the models to specific agricultural contexts. By incorporating domain-specific knowledge and expertise, these models have provided valuable insights for crop management and decision-making (Romero -vergel et al, 2022;Avneri et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield

Chandar,

Sivasankari,

Lakshmi

et al. 2023

Multidiscip. Sci. J.

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Wheat crop classification and prediction are important tasks for the optimization of crop yield and resource utilization. In this study, we propose an Artificial Neural Network (ANN) model integrated with Genetic Algorithm (GA) to predict and classify the wheat crop images of different ages. The dataset of 19,300 images was used, and the model was trained and tested using various performance evaluation metrics. The results show that the proposed ANN+GA model achieved the highest accuracy of 99.29% during the training phase and 98.65% during the testing phase. The model was also compared with other state-of-the-art machine learning models, and the proposed model was found to be superior in terms of accuracy, specificity, sensitivity, precision, and F-measure. The graph of training and testing accurateness and loss values in contradiction ofindividual epoch demonstrating the speediness of model convergence. Our proposed model is feasible and robust, giving better classification and crop forecast outcomes for numerous wheat crop age groups with least resource necessities. These findings could be useful for farmers and agricultural researchers in improving crop yield and resource management.

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