Using artificial neural network in determining postharvest LIFE of kiwifruit

Abstract

BACKGROUND Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes. RESULTS In present study, the predictive capabilities of artificial neural networks (ANNs) were evaluated with respect to assessing fruit firmness as a postharvest life index, with determinations made at four stages of storage: 1, 60, 120 and 180 days after harvesting. Single concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) on fruit (D1), all of these nutrient concentrations (D2), the ratios of the nutrient concentrations alone (D3), and a combination of nutrient concentrations and their ratios (D4), were considered. CONCLUSION The results obtained showed that fruit firmness at 1 and 60 days after harvesting was not influenced by nutrients. However, the ANN model estimated fruit firmness of 120 and 180 days, respectively, for D1 and D3 more accurately than for the D2 and D4 datasets. Application of D3 (nitrogen/calcium ratio) as the input dataset improved predictions of fruit firmness, with a correlation coefficient of 0.85 between the measured and estimated data. © 2019 Society of Chemical Industry

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

“…In particular, tools based on artificial intelligence-artificial neural networks (ANN), giving significantly lower prediction errors than in case of statistical methods-are very popular. Therefore, crop yield models are implemented in computer applications for precision agriculture and are becoming an important element of decision support systems [9][10][11][12][13][14][15][16][17][18].…”

Prediction method of shelf life of damaged Korla fragrant pears

“…It also has strong self-learning and adaptive ability and can adjust automatically by the existing data information network threshold and weights. This network helps to reduce the influence of artificial factors and makes the evaluation results more in line with the objective reality so that the neural network can make up for the defects existing in the above methods (Ali, Abbas, Perla, & Mahshad, 2019). In recent years, Back Propagation Neural Network (BPNN), General Regression Neural Network (GRNN), and Adaptive Network-based Fuzzy Inference System (ANFIS) have been extensively used in the prediction of fruits and vegetables, food rheology, food processing industry, fruit bruise prediction, fruit yield, and other aspects (Cheng et al, 2017;Farhad, Abdollah, Gholamhossein, & Ebrahim, 2020;Niu et al, 2020).…”

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

“…This is because the firmness of the fruit affects how long it can be stored after being harvested [37]. Thus, accurately assessing variations in firmness over time may assist in developing effective storage and marketing approaches for kiwifruit [18]. The firmness of kiwifruit can be influenced by various factors, including the mineral composition, particularly the calcium level, which has a significant impact on the inherent quality of the fruit and its ripening process [38].…”

“…The Vis/NIR spectroscopy technique has been utilized to evaluate the qualitative attributes of various fruits, such as apples, citrus fruits, and kiwifruits [12]. Non-destructive techniques like NIR, along with hyperspectral imaging, have been extensively employed to assess the qualitative characteristics of kiwifruit, such as firmness, pH, soluble solid content (SSC), and dry matter (DM) [2,[13][14][15][16][17][18][19][20][21][22]. The integration of Vis/NIR into machine learning (ML) algorithms enhances the effectiveness of learning, estimating, predicting, and classifying crucial quality parameters [23].…”