Potato Research最新文献

Abstract

The present study investigated the impact of edible coatings containing encapsulated antimicrobial and antibrowning agents on the quality and shelf life of fresh-cut potatoes. Polysaccharides extraction from potato peels using ultrasound/microwave-assisted extraction (UAE/MAE) was optimized via central composite design (CCD) and response surface methodology (RSM). Maximum starch recovery (216.55 ± 10.37 mg amylose/g potato peel) was achieved with UAE. Starch, pectin, and chitosan combination was the basis of the coatings. Rosemary essential oil (REO) and ascorbic acid (AA) were selected as antimicrobial and antioxidant agents, incorporated in free or encapsulated form via spray drying (SP) and electrospinning (EL). Over 14 days at 4 °C, coated potatoes exhibited improved appearance, reduced browning, enzyme activity, weight loss, hardness, and microbial load compared to controls. Notably, the potato starch-pectine coating with encapsulated REO/AA effectively inhibited browning and microbial growth, demonstrating the potential of edible coatings to enhance freshness and shelf life in fresh-cut potatoes.

Graphical Abstract

Potato is a food crop at a global scale, bearing a hefty importance for the food security and nutrition of millions of people worldwide. Nonetheless, some obstacles have to be overcome in the cultivation of potatoes, such as susceptibility to a number of diseases that affect quality and yield. Thus, sound disease management approaches are critical to protect potato crops and support maximum production. In this perspective, optimization techniques are vital in improving disease classification accuracy, thus helping in early detection and timely intervention. In this research, we suggest the hybridization of the Greylag Goose Optimizer (GGO) with the Grey Wolf Optimizer (GWO), which is called GGGWO, for the optimization of convolutional neural network (CNN) models for potato disease classification. Through our approach, we are seeking to enhance precision and timeliness in the diagnosis of diseases that will eventually lead to the development of appropriate crop management practices and sustainable agriculture. The performance of the GGGWO-CNN model is assessed in terms of accuracy and is compared to other optimization algorithms using statistical testing methods like ANOVA and Wilcoxon signed rank tests. The results exhibit the excellent performance of the GGGWO-CNN model with an accuracy of 0.9904 and a sensitivity of 0.9421 in identifying potato diseases accurately, highlighting its potential to aid farmers and general agriculture practitioners. Utilizing optimization techniques and CNN models, our research helps in the development of precision agriculture as well as the improvement of resilient potato cropping systems. The proposed method’s approach provides an exciting way of dealing with the problem of potato diseases. It provides an excellent platform for carrying out further studies on improving agricultural decision-making processes aimed at better crop health and productivity.

A 2-year (2017 and 2018) field lysimeter study was carried out to examine the effect of titanium dioxide nanoparticles (TiO₂ NPs) in irrigation wastewater on soil characteristics and potato (Solanum tuberosum L.) yield. Potatoes were planted in lysimeters (1.00 m × 0.45 m) in sandy soil and subjected to four treatments: freshwater (FW), wastewater (WW), freshwater + TiO₂ NPs (FW + NP) and wastewater + TiO₂ NPs (WW + NP), in triplicate. Potato tubers were harvested at maturity (120 days after planting). Both the TiO₂ NPs (with/without 1 mg L⁻¹ TiO₂ NPs) and irrigation treatments (FW vs. WW) had a significant effect (p ≤ 0.05) on chlorophyll content; however, they had little or no effect on soil physicochemical parameters (cation exchange capacity (CEC), pH and soil organic matter (SOM)), plant growth parameters (plant height, above-ground and root fresh weight) or yield (tuber weight, number of tubers and tuber grading). For both years, the total nitrogen content of the leaves increased consistently together with leaf chlorophyll content. Furthermore, tuber yield under FW, WW and WW + NP treatments were higher in the first year than in the second, likely due to higher growing season temperatures in the second year. This study furthers the knowledge on the impact of TiO₂ NPs on plant growth by showing that at 1 mg L⁻¹, irrigation water can increase greenness without inhibiting plant growth and yield. In addition, the potato plants, irrigated with water containing TiO₂ NPs, did not become infected with early and late blight diseases either year.

Rhizoctonia solani is a destructive soil-borne plant pathogen having a vast host range that infects various agricultural and horticultural crops. A total of 25 R. solani isolates were collected from a field of rice-potato rotation to determine their morphological variability, mycelial compatibility, cross-infectivity and fungicide sensitivity. Considerable variability in cultural and morphological features was observed among R. solani isolates collected from rice and potato crops. Rice isolates grew faster than potato isolates and hyphal width of both the host isolates varied from 6.67 to 9.37 μm. Significant variability was observed in sclerotial characteristics such as sclerotia colour, size, intensity and sclerotial distribution pattern on the Petri plates. Most of the isolates from both hosts produced micro (≤ 1.25 mm) size sclerotia and only few isolates produced macro (> 1.25 mm) size sclerotia. The sclerotial colour of most of the rice isolates was brown and some isolates exhibited black sclerotial colour. All the potato isolates produced brown-coloured sclerotia. Hyphal interaction studies revealed that potato isolates belonged to AG-3 and rice isolates belonged to AG1-IA group. Mycelial compatibility studies showed that 80.7% of the pairings were non-compatible, while 19.3% of the pairings exhibited a compatible interaction. Cross-infectivity revealed that R. solani potato isolates did not infect rice plants whereas rice isolates showed a varied degree of pathogenicity on potatoes and Rhizoctonia-like atypical symptoms were observed on the tuber surface. The EC₅₀ and EC₉₀ values of representative isolates from both hosts exhibited considerable variation with regard to sensitivity level against fungicides thifluzamide 24% SC and mancozeb 50% + carbendazim 25% WS. The outcomes of this study will help to develop management strategies and breeding programmes on varietal evaluation for effective management of Rhizoctonia diseases of potatoes and rice.

This paper introduces AI-PotatoGuard, an artificial intelligence (AI) tool which enhances the management of diseases in potatoes through the use of generative models and convolutional neural networks (CNN). In contrast to traditional practices, AI-PotatoGuard is a tool which provides the ability to detect potatoes in the early stages of the disease and also precisely detects the area affected. Through AI-PotatoGuard, it was observed that the conventional approach of identifying the diseases have been surpassed with 95% success observed in terms of getting the detection perfectly right and 85% in terms of getting the detection right at a much earlier stage. Traditional practices lagged with 75% detection right observation and a mere 50% in terms of detecting the disease early on. While traditional methods applied chemicals 2–3 times in practice in an area, the monitoring with AI-PotatoGuard resulted in only 2 out of 6 times in the same area. Hence, efficient and sustainable agriculture is achieved using AI.

The use of mineral phosphorus solubilising (MPS) bacterial inoculants, alone or supplemented with rock phosphate (RP), supports sustainable agricultural development and food security. This study investigates the combined effect of the MPS plant growth–promoting bacterium Pseudomonas rhizophila S211 and natural RP on potato growth and yield. Several genes potentially responsible for converting the insoluble phosphorus form in the soil into a soluble form for plant uptake were identified in the S211 genome including an open reading frame encoding a membrane-bound pyrroloquinoline-quinone-dependent glucose dehydrogenase (PQQ_mGDH) and a putative pyrroloquinoline quinone (PQQ) biosynthetic operon (pqqABCDEF). Additionally, the P. rhizophila genome harbors resistance genes related to osmotic, alkaline and metallic stresses, suggesting that strain S211 has strong environmental adaptability and RP metal bioremediation potential. The strain S211 showed ability to solubilise tricalcium phosphate (220 μg mL⁻¹) and Gafsa RP (207 μg mL⁻¹) with a decrease in pH (from 7 to 4). The effect of bioinoculant-RP co-supplementation on potato growth was optimised in greenhouse trials on alkaline soil using a mixture design. The results were further validated under field conditions using a randomised complete block design. The highest potato yield (62.6% increase) was obtained with binary mixture fertilisation (S211 inoculation and rock phosphate supplementation) compared with sole RP amendment. Thus, the application of natural RP in combination with MPS plant growth–promoting bioinoculant could be recommended as an ecofriendly alternative to pollution creating and costly chemical fertilisers.

Potatoes hold a significant role in domestic consumption, contributing to increased food security and serving as a source of income for smallholder farmers. The objective of this paper is to analyze the key determinants of potato production and market supply in the Woliso and Wonchi districts of the South West Shoa Zone in Ethiopia. Drawing data from 368 purposefully and proportionately sampled farmers across four kebeles, the study employs a multiple linear regression model to identify key determinants of potato production. Factors such as the education level of the household head, land allocation for potato cultivation, oxen per hour, usage of improved seed, and fertilizer application significantly and positively influence potato production. Additionally, a two-stage least square regression model reveals that the quantity of potatoes produced, family size, education of the household head, and distance to the nearest market significantly impact the quantity of potatoes supplied to the market. The findings underscore the need for targeted interventions, suggesting that government and stakeholders focus on initiatives promoting education, improved land management, and the provision of essential inputs to enhance both potato production and marketing in the region.

Crop intelligence and yield prediction of potato (Solanum tuberosum L.) are important to farmers and the processing industry. Remote sensing can provide timely information on growth status and accurate yield predictions during the growing season. However, there is limited documentation on the most suitable vegetation indices (VIs) and optimal growth stages for acquiring remote sensing imagery of potato. To address this knowledge gap, a systematic review was conducted. Original scientific manuscripts published between 2000 and 2022 were identified using various databases. The findings indicate that satellite imagery is the most widely used source of remote sensing data for tuber yield prediction, whereas unmanned aerial vehicle systems (UAVs) and handheld sensors are more frequently applied for growth monitoring. The normalized difference vegetation index (NDVI), red-edge chlorophyll index (CI_red-edge), green chlorophyll index (CI_green), and optimized soil-adjusted vegetation index (OSAVI) are the most frequently used VIs for the growth and yield estimation of potato. The tuber initiation stage was found to be the most appropriate stage for remote sensing data acquisition. This review will assist potato farmers, agronomists and researchers in selecting the most suitable VIs for monitoring specific growth variables and selecting the optimal timing during the growing season to obtain remote sensing images.

The simultaneous heat and mass transfer in the drying processes is a complicated unit operation. In the present study, the modelling of heat and mass transfer was conducted via investigating temperature distribution and moisture content along with calculating the heat and mass transfer coefficients during the combined infrared-hot air drying (IR-HAD) in the potato food model. The potato drying process was done with cylindrical cutting geometry (disc form) and through the recording of temperature changes in the samples during the process. The selected radiation intensity for this process was 400 and 800 W. Then three-channel thermocouples were placed in the centre, surface, and between these two points in the radius direction of the sample to record the temperature changes during the process. The sample temperature was recorded by a data logger at 200-s intervals. The results of the temperature recording indicated that temperature changes were significant in different parts of the sample along the radius. Mass and heat transfer coefficients, including the convective heat transfer coefficient (({h})), were calculated to be 5.32 and 9.98 W/m².K; similarly, the effective moisture diffusivity ({(D}_{eff})) was measured to be 5.18 × 10⁻⁸ and 9.93 × 10⁻⁸ m²/s, and the mass transfer coefficient (({h}_{m })) persisted at 0.005 and 0.010 m/s. An approximate doubling of the calculated coefficients was also observed by doubling the intensity of the radiation. The convective heat transfer coefficient has been introduced as the most important index in transport phenomena modelling and it is also applied in software simulation. Mathematical equations for the moisture transfer by Fick’s law and the heat transfer equation by Fourier’s law were solved using numerical methods and the results were applied in the computational simulation by COMSOL Multiphysics (5,3a). Then resulted profiles were also compared to simulated fried potato profiles. This simulation can help to control the temperature of the sample and it is further useful for quality control by reducing the moisture content.

Potato blight, sometimes referred to as late blight, is a deadly disease that affects Solanaceae plants, including potato. The oomycete Phytophthora infestans is causal agent, and it may seriously damage potato crops, lowering yields and causing financial losses. To ensure food security and reduce economic losses in agriculture, potato diseases must be identified. The approach we have proposed in our study may provide a reliable and efficient solution to improve potato late blight classification accuracy. For this purpose, we used the ResNet-50, GoogLeNet, AlexNet, and VGG19Net pre-trained models. We used the AlexNet model for feature extraction, which produced the best results. After extraction, we selected features using ten optimization algorithms in their binary format. The Binary Waterwheel Plant Algorithm Sine Cosine (WWPASC) achieved the best results amongst the ten algorithms, and we performed statistical analysis on the selected features. Five machine learning models—Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN)—were used to train the chosen features. The most accurate model was the MLP model. The hyperparameters of the MLP model were optimized using the Waterwheel Plant Algorithm Sine Cosine (WWPASC). The results indicate that the suggested methodology (WWPASC-MLP) outperforms four other optimization techniques, with a classification accuracy of 99.5%.