Potato Research最新文献

The type of oil used during deep-fat frying can play a unique role in the transfer phenomena due to their different physicochemical properties. The effects of three types of oil, including canola, sunflower, and soybean oils, on the momentum, heat, and mass transfer during frying of potato strips were evaluated. The results showed that the oil type did not have a significant effect on moisture loss and oil uptake. However, the velocity distribution patterns of the three oils were not the same. The average simulated velocity for soybean oil was higher than those of the other two, attributable to its higher density. The results showed that the surface temperature of potatoes was affected by oil type. Overall, the developed numerical simulation could help in a better comprehension of the deep-fat frying of potato strips with the ultimate aim of producing low-fat quality products.

The present investigation was aimed to develop an advanced refractance window drying (RWD) method for the drying of potato slices. A total of 17 experiments were carried out by using the Box-Behnken design (BBD) with RWD process parameters, i.e. drying temperature (75 °C, 85 °C, and 95 °C), potassium metabisulphite (KMS) concentration (0.5%, 1.0%, and 1.5%), and potato slice thickness (2 mm, 3 mm, and 5 mm). The effect of RWD process parameters on dehydration ratio, rehydration ratio, shrinkage ratio, total colour difference (TCD), and overall acceptability (OA) was analysed. Design-Expert software (ver. 13.0.1) was employed for numerical optimization of the experimental results. The optimized values for drying temperature (°C), KMS concentration (%), and slice thickness (mm) were found to be 85 °C, 0.5%, and 3 mm, respectively, and the corresponding responses were found to be 4.64, 3.45, 0.361, 9.956, and 4.64 for dehydration ratio, rehydration ratio, shrinkage ratio, TCD, and overall acceptability, respectively. A comparative analysis of optimized RWD (O-RWD) potato slices and conventionally dried (CD) potato slices was also conducted for proximate analysis, total colour difference (TCD), total sugar, textural properties (crispiness and hardness), water activity, and overall acceptability (OA). The results revealed that O-RWD potato slices were significantly higher in protein content, carbohydrates, total sugars, crispiness, and OA compared to CD potato slices. Overall, this study recommended that RWD provided better dried potato slices compared to CD.

This paper highlights why it is crucial to determine crop production using artificial intelligence for the growth of agriculture. In this paper, an elaborated ResNet-59 model has been developed to estimate potato harvests accurately. The dataset contained a global potato and tomato production data set that began in 1961 and ended in 2021; different deep learning architectures considered were ResNet-59, GoogLeNet, VGG-19, ResNet-50, VGG-16, and MobileNet. Collectively, the outcome of this ResNet-59 model’s improvement led to a general superiority with more minor mean squared errors, which were recorded as 0.0083, and a mean absolute error of 0.0762, a median of absolute errors amounted to 0.0750 along with an R² value equalling 99.05%. According to these results, precision agriculture is another area where ResNet-59 could be effective, thus promoting the rational distribution of resources, minimizing waste and increasing food security. It is epoch-making to deliberate on the capability of artificial intelligence to emancipate sustainable farming and future research.

Sprout suppression is a crucial aspect of maintaining postharvest Solanum tuberosum (potato) tuber quality. 1,4-dimethylnaphthalene (DMN) has demonstrated effective sprout suppression during long-term storage of potatoes. Its mode of action, however, remains unknown, and previous studies utilizing single cultivars preclude identification of a common response to treatment. Thus, the goal of this study was to identify common transcriptomic responses of multiple potato cultivars of varying dormancy lengths to DMN exposure during two dormancy stages. RNA-seq gene expression profiling supported differing sensitivity to DMN treatment dependent upon cultivar and dormancy stage. A limited number of genes with similar expression patterns were common to all cultivars. These were primarily identified in ecodormant tubers and were associated with cell cycle progression, hormone signaling, and biotic and abiotic stress response. DMN treatment resulted in significant upregulation of members of ANAC/NAC and WRKY transcription factor families. Investigation of affected protein-protein interaction networks revealed a small number of networks responsive to DMN in all cultivars. These results suggest that response to DMN is largely cultivar and dormancy stage-dependent, and the primary response is governed by a limited number of stress and growth-related genes and protein-protein interactions.

Accurate and timely acquisition of potato spatial distribution is crucial for growth monitoring and yield forecasting. Currently, prior knowledge-based methods are very simple and efficient without collecting reference data, but their mapping accuracy in complex cropping planting systems is unsatisfactory. Deep learning approaches have the ability to automatically learn multilevel spatial and spectral features. However, these approaches still face particular challenges in improving potato mapping accuracy due to the limitations of adaptive features and the scarcity of ground samples. This study proposed a potato mapping method integrating a multi-temporal deep learning network and prior knowledge to overcome the shortcomings of the two methods. Specifically, a novel deep learning network, spectral-spatial–temporal ensemble network (SSTEN), was developed for smallholder potato area mapping by embedding unique prior knowledge. To obtain multi-year potato mapping results, we proposed a concise and efficient temporal transfer framework that combines sample generation, SSTEN transfer learning, and agriculture statistics to produce highly accurate potato maps for sample-free years. Independent ground validation data from 2021 to 2022 suggested that the SSTEN achieved an overall accuracy (OA), F1 and Kappa of 91.65%, 92.67% and 0.82, respectively, and its average overall accuracy was superior to other methods. Potato planting areas obtained by SSTEN were highly consistent with the corresponding agricultural statistical area (R² > 0.87). The results showed that incorporating prior knowledge into SSTEN could improve the accuracy of potato mapping. We also investigated the potential of the proposed temporal transfer method for potato mapping. Our transfer method yielded a high OA of 86.46% and an area error (AE) of 7.94%. The study potentially provides technical references for smallholder potato mapping in similar agricultural regions worldwide.

Potatoes hold a significant position as one of the most important crops. Their value lies not only in their nutritional composition but also in their function as raw materials for various processing purposes. Furthermore, the cultivation of early potatoes carries considerable agrotechnical importance due to their ability to serve as the initial crop in intensive crop rotation, optimizing the utilization of agricultural soil. The primary objective of its production is to reach a consistent and high yield of premium quality. Additionally, the aim is to enter the market as early as possible and maximize profitability. To achieve these goals, producers utilize specific covering treatments such as mulching and plant covering to ensure earlier and safer production, thus maximizing profits. Our research aimed to determine the impact of different covering treatments (biodegradable mulch, agrotextile, low tunnel) on the chemical composition of early potato tubers. A 3-year field experiment was managed in Begeč (Serbia) with two early potato cultivars, Cleopatra and Riviera. The tested covering treatments significantly influenced the quality of early potatoes, by increasing the content of dry matter, starch, vitamin C, cellulose, and ash in the tubers and by reducting sugar and nitrate content.

The aim of this study is to analyse potato cultivation in South Asian Association of Regional Cooperation (SAARC) countries from 1961 to 2022, based entirely on secondary data from the Food and Agriculture Organization. By employing the ARIMA model, the research forecasts potato area and production up to 2030, with ARIMA (1, 1, 5) identified as the optimal model for both area and production in Afghanistan, Bangladesh, Sri Lanka, India, Myanmar, Nepal, Pakistan and China with a 95% accuracy level. By the year 2030, the projected potato area and production are expected to be 69,514.75 ha and 937,406.30 t in Afghanistan, 473,612.08 ha and 10,561,509.80 t in Bangladesh, 6,224,031.90 ha and 107,944,218.99 t in China, 2,447,779.92 ha and 61,310,173.10 t in India, 29,198.17 ha and 447,014.54 t in Myanmar, 220,857.06 ha and 3,885,372.21 t in Nepal, 464,614.77 ha and 10,154,642.65 t in Pakistan, and 4720.31 ha and 78,391.00 t in Sri Lanka. The trend analysis reveals non-linear patterns, with quadratic, exponential, and cubic trends standing out as the most suitable for depicting the series’ behaviour. The examination of instability levels showcases varying trends, with some countries experiencing a decrease while others show an increase. To ensure the long-term sustainability of potato cultivation, targeted strategies focusing on enhancing access to quality inputs, promoting efficient farming practices, and addressing volatility factors like market fluctuations and pest outbreaks are crucial. The study emphasizes the significance of monitoring and mitigating risks associated with potato cultivation to ensure stable and sustainable production. Sustainability is evaluated through the Sustainability Index, employing three methods, with the study highlighting the importance of maintaining productivity over an extended period. By providing insights into historical trends, volatility, and sustainability, this research offers a roadmap for well-informed judgement and calculated planning in the field of potato farming, ultimately contributing to food security and economic development in the SAARC region.

Estimating leaf nutrient concentration in field crops is essential to increase crop yield by optimum fertiliser application. Notably, these practices become more critical for short-cycle crops like potatoes (Solanum tuberosum L.), where conventionally, laborious in-field plant sampling and laboratory analysis take a long time. Multiple samples are frequently required to reach the field’s representation and reliability. The alternative technique of optical spectroscopy, which reports the canopy reflectance to the specific band of the electromagnetic spectrum, can be used to estimate the plant nutrient concentration. Previous studies have made such efforts using the electromagnetic spectrum’s visible to near-infrared (VNIR, 400–1100 nm) and short-wave infrared (SWIR, 1100–2400 nm) ranges. In this study, we are testing the ability of the spectroscopy with a full-range spectroradiometer (400–2400 nm) along with a comparison of VNIR and SWIR to estimate the total Kjeldahl nitrogen (TKN), phosphorus (P), potassium (K), and sulphur (S) nutrient concentration in freshly picked petiole/leaf samples of potato plants. Results show that the full-range spectrum predicted TKN with an accuracy of R² = 0.91 external validation (0.74 internal validation), followed by K, R² = 0.87 (0.69), P, R² = 0.86 (0.82), and S with R² = 0.75 (0.68). It was also reported that the maximum difference in the estimation accuracy among VNIR and SWIR was reported for K, where VNIR had R² = 0.48 (0.54) and SWIR had R² = 0.86 (0.80). This study lays a foundation for further development of models that can estimate the canopy nutrient concentration in the field with spectral reflectance and scale up these models with hyperspectral imaging.

The diseases that particularly affect potato leaves are early blight and the late blight, and they are dangerous as they reduce yield and quality of the potatoes. In this paper, different machine learning (ML) models for predicting these diseases are analysed based on a detailed database of more than 4000 records of weather conditions. Some of the critical factors that have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, and atmospheric pressure. These types of data relationships were comprehensively identified through sophisticated means of analysis such as K-means clustering, PCA, and copula analysis. To achieve this, several machine learning models were used in the study: logistic regression, gradient boosting, multilayer perceptron (MLP), and support vector machine (SVM), as well as K-nearest neighbor (KNN) models both with and without feature selection. Feature selection methods such as the binary Greylag Goose Optimization (bGGO) were applied to improve the predictive performance of the models by identifying feature sets pertinent to the models. Results demonstrated that the MLP model, with feature selection, achieved an accuracy of 98.3%, underscoring the critical role of feature selection in improving model performance. These findings highlight the importance of optimized ML models in proactive agricultural disease management, aiming to minimize crop loss and promote sustainable farming practices.

This study explores soil and fertilizer management techniques using winter cereal rye and Chlorella sorokiniana microalgae biofertilization alongside mineral and organic fertilizers for spring–summer potato cultivation in both conventional (CONV) and organic (ORG) production systems in subtropical environments. Traditional soil management, with a fallow period followed by subsoiling, plowing, and harrowing, served as the reference standard for comparisons with four alternative methods in CONV and ORG systems. In the CONV system, cereal rye plants were terminated with glyphosate and the alternative soil managements included (i) incorporating chopped cereal rye with standard soil tillage, (ii) no-till planting into chopped cereal rye, (iii) planting into chopped cereal rye after soil chiseling, and (iv) mulching chopped cereal rye residues on the ridges of potato planted after standard soil tillage. In the ORG system, the alternatives included (v) incorporating fresh cereal rye with standard soil tillage, (vi) no-till planting into standing fresh cereal rye plants, (vii) no-till planting into cereal rye terminated with a knife roller, and (viii) mulching whole cereal rye plants between the ridges of potato planted after standard soil tillage. Each soil management was combined with treatments of no fertilization or either mineral or organic fertilization with or without microalgae application. Amid severe water constraints, particularly due to La Niña events, standard soil tillage in CONV and no-tillage in ORG both on cereal rye crops respectively increased (39.5%) total tuber yield and number of tubers per plant (18.8%), showing themselves as potential conservation soil managements to potato crop. Microalgae with respective fertilizer application exclusively associated with chopped cereal rye residues on hills in CONV and with no-till planted into fresh plants of cereal rye in ORG favored tuber filling.