Search

Appropriate organic amendment management is essential in potato production to increase soil productivity and potato (Solanum tuberosum L.) quality. The objectives of this two-year field study (2004–2005) were to evaluate the effects of organic amendment applications with or without mineral N fertilizer addition on potato yield, N uptake, N use efficiency (NUE), and on disease incidence. The experimental design was a split-plot, which included nine treatments with four different organic amendments applied in fall 2003 or in spring 2004 at a rate of 40 Mg ha−1 (wet basis) and an unamended control in main plots, and N fertilizer rates (0 and 90 kg N ha−1) in sub-plots. Organic amendments consisted of fresh cattle manure (FCM), composted cattle manure (CCM), paper mill sludge with C/N ratio 15 (PMS2) applied alone (0 kg N ha−1) or supplemented with mineral fertilizer at a rate 90 kg N ha−1. The N fertilizer rate in the unamended control consisted of 0 and 150 kg N ha−1. No organic amendments were applied in 2005 to evaluate residual effects. Fall and spring applications of FCM, CCM and PMS alone significantly increased N uptake and potato marketable yields by 2.5 to 16.4 Mg ha−1, compared to the unfertilized control. Combining organic amendments with N fertilizer at 90 kg N ha−1 increased potato yields, N uptake, and specific gravity, which were comparable to those obtained in mineral N fertilizer treatments (150 kg N ha−1). Residual effects of organic amendments alone had no significant effects on potato yields or on N uptake compared to the unfertilized control. Potato NUE for FCM, CCM and PMS ranged from 6% to 25% in the first year and from 2% to 8% in the residual year. The NUE values were higher for PMS with lower C/N ratio compared to FCM and CCM. This study did not show any difference between organic amendment applied in spring or fall on potato yield and quality. It was also demonstrated that mineral N fertilizer needed to be combined with organic amendments to sustain potato yield and N nutrition. This study showed that appropriate organic amendment management could increase potato yield and quality and reduce N fertilizer requirement.

Novel Carbon quantum dots-graphite composite ink-based Screen-printed electrodes (CQDs/SPEs) were used to assemble a highly sensitive electrochemical aptasensor against chlorpyrifos (CPF). The aptasensor showed a broad linear range from 1 pM (0.445 ng/ml) to 500 nM (0.22 mg/ml) with a detection limit (LOD) 0.834 pM (0.37 ng/ml); sensitivity 21.39 μA pM−1 cm− 2 and with good linearity of R2 = 0.973. Moreover, the aptasensor's showed better selectivity among few other pesticides. Further, the aptasensor electrode showed high stability for five months when stored at 4 °C. In the final step, the aptasensor's ability to identify CPF in real samples was evaluated on spiked potato (Solanum tuberosum) extract samples. Potato extract spiked with CPF in the electrochemical aptasensing platform showed excellent linearity of R2 = 0.981. The developed aptasensor showed good response to without spiked potato extract with increasing volumes. Hence, the developed aptasensor demonstrated reasonable applicability in real food and agriculture samples. • Chlorpyrifos (CPF), an organophosphate pesticide, is known for detrimental effects on environment raising global health concern. • Electrochemical based disposable screen printed aptasensor is developed for the detection of chlorpyrifos pesticide. • The CQDs/SPE aptasensor was fabricated using carbon quantum dots (CQDs) through drop cast method. • The aptasensor exhibited a wide linear range (1 pM-500 nM) with LOD (0.834 pM) and high sensitivity (21.39 μA pM−1 cm− 2). [ABSTRACT FROM AUTHOR]

[Display omitted] • Fabrication of paper disc interfaced immunodevice for detecting diverse biomarker. • Highly sensitive and affordable disease detection tool. • Validated with range of spiked and real samples. • Universal sensing ability was accessed using C-reactive protein and potato viruses. The detection of specific biomarkers is used in various phases of the diagnosis of plant and human diseases, from prognosis to monitoring. Herein, we report a Prussian blue nanocube-modified immunodevice interfaced with a paper disc for the detection of plant biomarkers via streptavidin-biotin recognition. The detection ability of the immunodevice was assessed using Potato virus X as a model biomarker and analyzed using cyclic voltammetry, differential pulse voltammetry, and electrochemical impedance spectroscopy. The immunodevice displayed excellent performance for Potato virus X detection with a detection limit of 0.92 nM (3S/N). The selectivity of the fabricated Potato virus X immunodevice was investigated using closely associated antigens, such as potato aucuba mosaic virus, Potato virus Y, and Potato virus A. The Potato virus X immunodevice exhibited ∼ 90 % recovery in spiked complex plant samples with a relative error of ∼ 9 %. Furthermore, the immunodevice was used to screen for Potato virus X in 10 samples from potato tubers and leaves. The paper-disc-interfaced immunodevice was also evaluated by detecting other biomarkers, such as potato aucuba mosaic virus in plant diseases and C-reactive protein in human ones. This immunodevice may allow the on-site monitoring of diverse biomarkers by simplifying the current point of care diagnostic tools. [ABSTRACT FROM AUTHOR]

The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain. [ABSTRACT FROM AUTHOR]

Within a few years of the establishment of the convict settlement at Sydney Cove, the potato became one of the staple crops of the population due to its relatively high yield and the prior experience of the convicts and free settlers with growing the crop. In 1894, Henry Tryon described a new disease in southern Queensland that caused rapid wilting of plants, a ring of slightly translucent tissue just below the surface of affected tubers, oozing of a thick, white fluid from the 'eyes', and ultimately rotting of the tubers. It soon became known as 'Tryon's disease'. He found that a microbe (bacterium) was always associated with affected tubers and stems, provided a very brief description of the bacterial cells and named the microbe Bacillus vascularum solani. A few years later the American scientist Erwin Frink Smith wrote a paper on a new disease (brown rot) of solanaceous plants including the potato and tomato, in which he called the causal agent Pseudomonas solanacearum , now known as Ralstonia solanacearum. Smith dismissed Tryon's prior claim to the discovery of the disease with some of his comments being personal and scathing. Tryon had the last word, however, cloaking his response in restrained and somewhat convoluted tones. In 1894, the Queensland government entomologist, and later vegetable pathologist, Henry Tryon (1856–1943) discovered a new disease that caused potato tubers to become rotted and putrid. He consistently found bacterial cells in a thick mucilaginous gum in the vascular tissues of wilted stems and infected tubers, and gave it the name Bacillus vascularum solani. The American bacteriologist Erwin Frink Smith would not accept Tryon's discovery, instead naming the causal agent Pseudomonas solanacearum. That bacterium, now called Ralstonia solanacearum is a significant plant pathogen worldwide. Photograph by an unknown person. [ABSTRACT FROM AUTHOR]

Traditional potato grading in China relies mostly on manual sorting, which is labor-intensive, time-consuming, costly, and inefficient. To enhance the operational performance of potato-grading devices, this paper focuses on optimizing the slide rail structure, which is the key component of a self-developed first-generation potato-grading device. A five-factor, three-level orthogonal experiment was designed, with the experimental factors being the height of the horizontal slide rail, angle of the first-stage inclined slide, angle of the second-stage inclined rail, chain horizontal movement speed, and conveyor belt speed. The indoor experiments were conducted using grading accuracy and grading efficiency as the experimental indicators. On the basis of the analysis of the orthogonal experiment results, two relatively optimal solutions were obtained, and validation experiments were conducted. The validation results show that when the height of the horizontal slide rail was 185 mm, the angle of the first-stage inclined rail was 4°, the angle of the second-stage inclined rail was 2.5°, the horizontal movement speed of the chain was 700 mm/s, and the movement speed of the conveyor belt was 275.60 mm/s, the performance of the movable rotating plate (MRP)-type grading device for potatoes reached its optimum. At this point, the grading accuracy was 94.88%, and the grading efficiency was 13.9477 t/h. Compared with the first-generation grading device, the optimized grading device achieved an improvement of 3.84% in grading accuracy and 12.94% in grading efficiency. The research methodology provided in this paper serves as a reference for the performance optimization of potato-grading devices. [ABSTRACT FROM AUTHOR]

Vegetative propagation of potatoes makes it possible for potato viruses to be transmitted through tubers. Potato virus A (PVA) is one of these viruses, which belongs to the Potyvirus genus in the Potyviridae family. Potato tuber yield can be reduced by 30–40% by PVA alone. Losses can be further exacerbated by potato virus X and/or potato virus Y infection. PVA is transmitted primarily by several species of aphids in non-persistent manner. With the aim of resolving this problem, we developed one-step reverse transcription-recombinase polymerase amplification (RT-RPA), a highly sensitive and cost-effective method for detecting PVA in both potato tubers and leaves. Detection and amplification are performed using isothermal conditions in this method. There was good amplification of the coat protein gene in PVA with all three primers tested. To conduct this study, a primer set that can amplify specific 185 base pair (bp) product was selected. PVA detection was optimized by 30-min amplification reactions, which showed no cross-reactivity with other potato viruses. A simple heating block or water bath was used to amplify PVA product using RT-RPA at a temperature range of 38–42 °C. In comparison to conventional reverse transcription-polymerase chain reaction (RT-PCR), the newly developed RT-RPA protocol exhibited high sensitivity for both potato leaves and tuber tissues. Using cellular paper-based simple RNA extraction procedure, the virus was detected in leaf samples as efficiently as purified total RNA. We also found that combining LiCl-based RNA precipitation with cellular paper discs allowed us to successfully optimize RNA extraction for one-step RT-RPA for detecting PVA in tubers. Tests using this simplified one-step RT-RPA method were successfully applied to 300 samples of both leaves and tubers from various potato cultivars. In our knowledge, this is the first report of an RT-RPA assay utilizing simple RNA obtained from either cellular disc paper or LiCl coupled with cellular disc paper to detect PVA. As a result, this method was equally sensitive and specific for detecting PVA in potatoes. The developed RT-RPA assay is more versatile, durable, and do not require highly purified RNA templates, thus providing an effective alternative to RT-PCR assays for screening of germplasm, certifying planting materials, breeding for virus resistance, and real-time monitoring of PVA. [ABSTRACT FROM AUTHOR]

Plastic film mulching technology occupies a prominent position in China's agricultural production and plays an important role in improving crop yields, which can increase farmers' income and ensure national food security. As the current residual film recycling mechanism is incomplete, the accumulation of residual film in the soil has gradually increased, and the problem of residual film pollution has posed a serious threat to agricultural production and the natural environment. Ridge mulching cultivation is one of the main planting patterns for grain and oil crops, such as peanuts, potatoes, and maize, in China. In this paper, the planting patterns, mulching conditions, characteristics, and distribution of residual film of the typical ridge mulching crops, such as peanut and potato, were analyzed. The merits and demerits of various kinds of residual film recycling machines and the application status and operation quality of peanut and potato residual film recycling machines are summarized. Problems and suggestions for the mechanized recycling of residual film were raised. This study may provide ideas for the development of residual film mechanization recycling of ridge mulching crops. The aim of this study is to propose practical solutions by analyzing the current status and existing problems of mechanized residual film recovery in order to reduce residual film pollution, protect the environment, and achieve sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The Netherlands has a world-leading position in potato breeding, but little is known about the factors that led to this success. This paper analyses the factors that have influenced the development of potato breeding in the Netherlands. This study is based on research of the grey and scientific literature and interviews with various representatives from the Dutch potato breeding sector. We distinguish four periods: (i) Before 1888, no potato breeding in the Netherlands existed whereas in other countries first crosses occurred. (ii) 1888–1940, more individuals started breeding out of interest and hobby to overcome the commonly observed degeneration of potato. (iii) 1940–1967 the emergence of a corporate set up of breeding by private companies collaborating with small breeders. (iv) 1967–present, towards full-fledged breeding industry supported by the new Seeds and Planting Materials Act (ZPW) in 1967 including the breeders' rights. Many factors including cultural practices, diseases, and market that determine the strategy of breeding have been analysed. The development is most of all 'crop driven' to maintain the level of production. But it was also 'export driven' leading to the development of an export-oriented seed potato sector. The conclusion is that three elements were dominant in the development of a strong potato breeding sector: (1) the broad cooperation among all players in the potato chain, (2) the design of the institutional infrastructure, and (3) the remuneration of the breeding work through legislation regarding plant breeders' rights. The study ends with an outlook on future trends, one of them leading from an open to a more closed business culture. [ABSTRACT FROM AUTHOR]

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net's backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method. [ABSTRACT FROM AUTHOR]

The paper presents a series of aspects regarding the evolution of potato production and trade worldwide, for the period 2012-2019. In the present study were analyzed a series of benchmarks for the world sector of potatoes production and trading. The main indicators that have been presented and analyzed are: areas cultivated with potatoes worldwide; total potato production; average production per hectare of potatoes; consumption of potatoes; potatoes world trade. The world potato production sector has undergone substantial changes, but it remains one of the important sectors of activity, as the potato occupies a key place on the scale of important foods for the world's population food. According to statistics, China is the leader in the ranking of potato production. Data taken from the FAO website were used in the paper. [ABSTRACT FROM AUTHOR]

Appropriate water supply is crucial for high-yield and high-quality potato tuber production. However, potatoes are mainly planted in arid and semi-arid regions in China, where the precipitation usually cannot meet the water demand throughout the growth period. In view of the actual situation of water shortage in these areas, to monitor the water status of potato plants timely and accurately and thus precisely control the irrigation are of significance for water-saving management of potatoes. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, the canopy spectral reflectance and plant water content were measured under five irrigation treatments. The spectral parameters that respond to plant water content were selected, and a hyperspectral water diagnosis model for leaf water content (LWC) and aboveground water content (AGWC) of potato plants was established. It was found that potato tuber yield was the highest during the entire growth period under sufficient irrigation, and the plant water content showed a downward trend as the degree of drought intensified. The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength, where the reflectance varied significantly under different water treatments and decreased with decreasing irrigation. Six models with sensitive bands, first-order derivatives, and moisture spectral indices were established to monitor water content of potato plants. The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data; and 0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data. These six models can all predict the water content of potato plants, but SVM is the best model for predicting LWC of potato plants. These results are of great significance for guiding precision irrigation of potato plants at different growth stages. [ABSTRACT FROM AUTHOR]

The study assesses the gender implications of a target product profile for table potato in Kenya. Breeding programs mostly emphasize farmers’ trait requirements and rarely pay attention to other value chain actors’ preferences. This partially contributes to the low uptake of improved varieties. Therefore, efforts are required to assess the gender implications of crop product profile proposals during development, testing, and dissemination. In this paper, we assess the gender implications of traits targeted by a potato breeding program in Kenya using the G+ tools. The study applies the G+ product profile tool to examine the instances the selected traits may exacerbate gender disparity along four domains: unpaid labor, access to employment opportunities, requiring extra inputs and control over benefits. We use mixed methods including a review by social scientists, key informant interviews and a multistakeholder workshop to gather insights from female and male farmers, breeders, and other stakeholders. Findings show that pest, disease resistance, and shelf-life traits benefit women and men in the target customer segments. On the other hand, earliness, dry matter, and yield could increase drudgery for women. The traits increase demand for women’s unpaid labor during harvesting, sorting and food preparation time while also displacing women from profitable nodes. We recommend that gender-responsive strategies accompany the release of the target variety to mitigate inequities and enhance adoption. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large error and redundancy in the multi-node data acquisition of multi-greenhouse photo growth environmental information, a three-level fusion algorithm based on adaptive weighting, an LMBP network, and an improved D-S theory is proposed. The box-and-line graph method recognizes the original data and then replaces it based on the mean value method; the air temperature, humidity, and light intensity measurements are unbiased estimations of the true value to be estimated, so the first level of fusion chooses the adaptive weighted average algorithm to find the optimal weights of each sensor under the condition of minimizing the total mean-square error and obtains the optimal estimation of the weights of the homogeneous sensors of a greenhouse. The Levenberg–Marquardt algorithm was chosen for the second level of fusion to optimize the weight modification of the BP neural network, i.e., the LMBP network, and the three environmental factors corresponding to "suitable", "uncertain" and "unsuitable" potato growth environments were trained for the three environmental factors in the reproductive periods. The output of the hidden layer was converted into probability by the Softmax function. The third level is based on the global fusion of evidence theory (also known as D-S theory), and the network output is used as evidence to obtain a consistent description of the multi-greenhouse potato cultivation environment and the overall scheduling of farming activities, which better solves the problem of the difficulty in obtaining basic probability assignments in the evidence theory; in the case of a conflict between the evidence, the BPA of the conflicting evidence is reallocated, i.e., the D-S theory is improved. Example validation shows that the total mean square error of the adaptive weighted fusion value is smaller than the variance of each sensor estimation, and sensors with lower variance are assigned lower weights, which makes the fusion result not have a large deviation due to the failure of individual sensors; when the fusion result of a greenhouse feature level is "unsuitable", the fusion result of each data level is considered comprehensively, and the remote control agency makes a decision, which makes full use of the complementary nature of multi-sensor information resources and solves the problem of fusion of multi-source environmental information and the problem of combining conflicting environmental evaluation factors. Compared with the traditional D-S theory, the improved D-S theory reduces the probability of the "uncertainty" index in the fusion result again. The three-level fusion algorithm in this paper does not sacrifice data accuracy and greatly reduces the noise and redundancy of the original data, laying a foundation for big data analysis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a precise collision model of potatoes falling onto a separation sieve to assess their damage characteristics. A viscoelastic‐plastic mechanics model is created by integrating the collision process with the potato soil separation device. Test factors include fall height, potato mass, fall direction, and varieties. Potato deformation and damage volume serve as evaluation indices in single‐factor tests to explore their relationships with these factors. The model analyzes the collision loading and unloading process, studying relationships between parameters and test factors, examining the potato collision damage mechanism. Predictive equations for potato damage volume based on fall height, potato mass, and plasticity ratio λp are derived: V1 = 1606λp–537, V2 = 4751λp–2087, and V3 = 12617λp–5303. This study provides a theoretical foundation for analyzing potato collision damage and offers technical references for optimizing potato harvesting machinery. Practical applications: Damage resulting from the mechanical harvesting process significantly impacts the quality and economic worth of the tubers. Currently, research on constructing a mechanical model of potatoes, treating them as viscoelastic plastic bodies, and integrating the collision process with potato soil separation devices is limited. Thus, the objective of this paper is to investigate the correlation between various test factors and potato damage via drop tests, and to establish a viscoelastic–plastic mechanical model. Additionally, the study further explored the correlation between model parameters and tuber damage, resulting in the establishment of a mathematical model for predicting the extent of damage. This study provides a theoretical basis for an in‐depth analysis of the collision damage mechanism of potato, and a technical reference for the design and optimization of potato harvesting machinery. [ABSTRACT FROM AUTHOR]

Potatoes are developed vegetatively from tubers, and therefore potato virus transmission is always a possibility. The potato leafroll virus (PLRV) is a highly devastating virus of the genus Polerovirus and family Luteoviridae and is regarded as the second-most destructive virus after Potato virus Y. Multiple species of aphids are responsible for the persistent and non-propagating transmission of PLRV. Due to intrinsic tuber damage (net necrosis), the yield and quality are drastically diminished. PLRV is mostly found in phloem cells and in extremely low amounts. Therefore, we have attempted to detect PLRV in both potato tuber and leaves using a highly sensitive, reliable and cheap method of one-step reverse transcription-recombinase polymerase amplification (RT-RPA). In this study, an isothermal amplification and detection approach was used for efficient results. Out of the three tested primer sets, one efficiently amplified a 153-bp product based on the coat protein gene. In the present study, there was no cross-reactivity with other potato viruses and the optimal amplification reaction time was thirty minutes. The products of RT-RPA were amplified at a temperature between 38 and 42 °C using a simple heating block/water bath. The present developed protocol of one-step RT-RPA was reported to be highly sensitive for both leaves and tuber tissues equally in comparison to the conventional reverse transcription-polymerase chain reaction (RT-PCR) method. By using template RNA extracted employing a cellular disc paper-based extraction procedure, the method was not only simplified but it detected the virus as effectively as purified total RNA. The simplified one-step RT-RPA test was proven to be successful by detecting PLRV in 129 samples of various potato cultivars (each consisting of leaves and tubers). According to our knowledge, this is the first report of a one-step RT-RPA performed using simple RNA extracted from cellular disc paper that is equally sensitive and specific for detecting PLRV in potatoes. In terms of versatility, durability and the freedom of a highly purified RNA template, the one-step RT-RPA assay exceeds the RT-PCR assay, making it an effective alternative for the certification of planting materials, breeding for virus resistance and disease monitoring. [ABSTRACT FROM AUTHOR]

Copyright of INMATEH - Agricultural Engineering is the property of INMATEH - Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: Crop switching is an important climate change adaptation strategy. New crops may need to be cultivated to provide food security as traditional staple crops become less suited to the changing climate in the bread baskets of many countries. For example, potato farming in the Philippines is being scaled up to supplement the cultivation of rice to meet the needs of a growing population. Since new crops introduced for this purpose can also be vulnerable to invasive pests, it is necessary to develop methods for planning pest management strategies that consider the complex interactions that can occur in farm ecosystems. In this paper, we develop a graph theoretic model for assessing pest management options for the prospective case of the entry of the Colorado potato beetle in potato farms in the Philippines. Two biological control agents and use of chemical pesticides are considered as alternative strategies. The model results indicate that the biological control strategies outperform the use of chemical pesticides. The concurrent use of both biological control agents could be less effective due to competition between the two species. Crop shifting is considered as an important strategy to secure future food supply in the face of climate change. However, use of this adaptation strategy needs to consider the risk posed by changes in the geographic range of pests that feed on selected crops. Failure to account for this threat can lead to disastrous results. Models can be used to give insights on how best to manage these risks. In this paper, the socioecological process graph technique is used to develop a network model of interactions among crops, invasive pests, and biological control agents. The model is applied to a prospective analysis of the potential entry of the Colorado potato beetle into the Philippines just as efforts are being made to scale up potato cultivation as a food security measure. The modeling scenarios indicate the existence of alternative viable pest control strategies based on the use of biological control agents. Insights drawn from the model can be used as the basis to ecologically engineer agricultural systems that are resistant to pests. [ABSTRACT FROM AUTHOR]

Motivation: Crop diseases pose a critical threat to global food security, causing substantial agricultural yield losses. Manual disease identification is labor-intensive and requires expertise, hindering timely and accurate detection. Problem Gap: Traditional methods for identifying plant diseases are time-consuming and demand specialized knowledge. The need for a more efficient, automated, and accurate approach is evident to minimize crop losses and ensure food security. Contribution: This research paper addresses the challenge of plant disease detection by proposing a novel approach that combines image processing and deep learning techniques. The primary contribution is a sophisticated deep neural network ensemble model, which integrates Residual Network, MobileNet, and Inception models. This ensemble architecture is designed to maximize accuracy and robustness in disease classification. The model is trained on an extensive dataset containing images of both healthy and infected potato leaves. It accomplishes the precise categorization of leaves into three classes: healthy potato leaves, infected potato leaves (two classes). The model's proficiency lies in its ability to discern intricate leaf features, colors, and types, enabling it to differentiate between healthy and potentially diseased leaves. This research makes a significant contribution to the development of automated disease detection systems. By bridging the gap between manual identification and advanced technology, it presents a promising solution for early disease detection and prevention. Ultimately, this approach can foster increased agricultural productivity, ensuring enhanced food security on a global scale. Results: Results demonstrate the proposed model's effectiveness, achieving an impressive overall accuracy of 98.86%. This high accuracy attests to the model's competence in precisely detecting and classifying potato diseases. By harnessing the potential of ensemble-based deep learning and image processing, this research introduces an innovative tool for agriculturalists and researchers. [ABSTRACT FROM AUTHOR]

Summary: Potato is a tuber that, due to its great variety, is available in the markets all year round. Moreover, it is a widely consumed product worldwide on the account of its affordability and versatility. Therefore, studying it in depth is significant from not only a nutritional or economic point of view, but also for considering its potential environmental impact. In this paper, a Life Cycle Assessment (LCA) model is developed to assess the environmental impact of a model 5 kg bag of fresh washed potatoes. It considers all phases of potato processing, from the time the potato enters the facility until it leaves the gate (gate‐to‐gate LCA). The phase of the process with the greatest environmental impact is the cleaning and classification phase, mainly due to the significant consumption of water used in the cleaning process. The general consumption and storage phases are also relevant environmental impact phases to consider. In the case of these phases, the electricity consumption resulting from the operation of the facilities and product cooling is particularly noteworthy. After establishing the critical points, based on the environmental impact perspective, this paper proposes three measures to reduce the environmental impact of the process. The application of these three measures would reduce the environmental impact by 40% in environmental impact categories such as acidification or by 32% in the global warming category. [ABSTRACT FROM AUTHOR]

This paper aims to present the use of the guttation test method to establish the relationship between guttation and potato (Solanum tuberosum L.) yield. The laboratory tests (in vitro L.) under household conditions were carried out. To assess the state of the potato (variety 'Ando') yield the field (0.07 ha) of the family farm "Miili" were used. Assessment of the state of guttation plant barley (Hordeum vulgaris L.) variety 'Anni' by using of hydro-thermostat in conditions adapted hose hold laboratory (according to the generally known a bad epidemiological situation) were carried out. The test sites were located on a light sandy loam of Molli-Calcaric Cambisol (WRB) with areas of the field in the presence of soil samples No. 1 and 2. We have found that the maximum yield of potato 'Ando' on a plot of soil sample No. 1 (15 900 kg ha–1 or 1.00 of relative units) was obtained. At the same time, the minimum yield of soil sample No. 2 of the potato tubers was 3900 kg ha–1 (0.25 of relative units). To ranking score of the soil fertility level of the studied potato field, additional laboratory experiments were carried out, with extremely humus-rich soil (the guttation droplet imprint on the filter paper was 117.6 mm² to that was equated to 1.00 of relative units) and with sand the guttation droplet imprint – 37.0 mm² and 0.31 of relative units, respectively. The results of laboratory tests by using of guttation method with barley (variety 'Anni') carried out. The soil samples have been taken from the same areas of the field. In this case, for soil sample No. 1 the average area of the guttation droplet imprint on the filter paper was 55.1 mm² to that was equated to 0.47 of relative units and for soil sample No. 2 – 42.9 mm² or 0.36 of relative units, respectively. The results of the research have shown that concerning cultivating potatoes (variety 'Ando') and guttation experiments with barley (variety 'Anni') under conditions of soil samples No. 1 and 2 of the potato field a quite reliable relationship between guttation and the yield of potato tubers (P <0.001, R² = 0.98) was obtained. To assess the different levels of soil fertility for soil samples No. 1 and 2 in the potato field taking as a reference soil with the highest possible fertility (humus-rich soil) and with the lowest possible fertility (clean sand) it was revealed that guttation of soil sample No. 1 of the potato field was 1.6 times inferior according to the results of guttation of the humus-rich soil. Analytical calculations have shown that if we are dealing with a humus-rich soil where potato 'Ando' cultivation would be carried out under the conditions of classical organic farming then the yield of potato tubers would be 22 880 kg ha–1 or 30 Mg ha–1 rounded. The novelty of our research was the development of a method for assessing the yield of potato by using the guttation test method. [ABSTRACT FROM AUTHOR]

Sustainable food and agriculture is emphasized in recent times to provide for present and future generations with a lesser impact on the environment. Equal importance is given to environmental impact assessment of crop production and related processes to understand their effect on the environment and natural resources and formulate measures and policies to mitigate the same. Water footprint assesses the impact of products and processes on freshwater resources. This paper is an attempt to review available research on water footprint assessment of root and tuber crops and their products majorly potatoes, cassava and sweet potatoes. Root and tuber crops are gaining importance as major food security crops and are a more sustainable alternative to cereal crops in terms of water footprint. Apart from this, root and tuber crops (majorly, cassava and potato) are also gaining importance as feedstocks for biofuel production; however, they have a higher water footprint than sugar crops (sugar beet, sugar cane) used. Further research is required on the water footprint assessment of root and tuber crops especially crops other than potato, cassava and sweet potato and their products to help to achieve their full potential as sustainable food and energy crops. Efforts to develop ecocrop models to assess the suitability of tuber crops in different agroecological locations and effective utilization of green water to achieve more crop per drop and to reduce the water footprint are to be strengthened. [ABSTRACT FROM AUTHOR]

Food tourism includes various activities and attractions linked to food. Potatoes can also be also linked to tourism, or food tourism as testified by special Victorian Potato Industry Tour organized in and around Melbourne, Australia or Rural Community Tourism in the Potato Park; a tour around Cusco, Peru; various culinary events dedicated to potatoes in Slovenia and many others. This paper contributes to the knowledge on tourism and specifically food tourism by proposing to "open" a new niche of research based on specific foods or their ingredients, which up to now seems to have touched only a very few specific foods (or drinks) and associated ingredients—for instance, the case of grape/wine is exemplar. However, many other specific foods or ingredients could be singularly one-by-one) researched to examine their role in tourism. This paper discusses the role in tourism/food tourism of a "humble tuber", the potato. This is a desktop research which draws on previous literature, organizational and institutional documents and information and data, such as the list of museums around the world. The aim is to analyse the knowledge from various disciplines linking the potato to tourism to understand the various ways in which it can directly or indirectly have a role in tourism. The article presents the potato including its (social) history, gastronomy in relation to tourism/food tourism. The article argues that the potato should be promoted given its various uses and universality of application as a food and as an ingredient. [ABSTRACT FROM AUTHOR]

The combined fertilizer application is a strategy that combines both organic and inorganic plant nutrients to achieve higher crop productivity, prevent soil degradation and thereby aid encounter future food supply needs. The main objective of this paper is to review the role of combined fertilizer application on soil fertility, growth and yield of potato. This paper is done by assessing various kinds of published and unpublished materials and relevant information from different literature sources like libraries, research report, journals, books and Internet center. It has integrated use of organic and inorganic fertilizer affects the tuber yield and its attributing characters like an average number of tuber/hills, average tuber weight/hill, marketable tuber number and total dry matter yield. The use of farmyard manure and vermicompost along with inorganic fertilizers is superior to the application of either farmyard manure or vermicompost alone. Application of organic manure along with inorganic fertilizer increased organic carbon, available P and K. But the application of inorganic fertilizer alone had no significant effect on the fertility status of the soil. The reports revealed that integrated use of organic manure and reduced rates of inorganic fertilizer significantly improved potato productivity and improved the fertility status of the soil and could save inorganic fertilizer for sustainable potato production in potato growing areas. [ABSTRACT FROM AUTHOR]

SITE SPECIFIC is a farming management practice with the potential to address management challenges, this approach leverages precise and timely information on agricultural resources to observe and measure variability in field crops. Site -Specific management zones one of the most important pillars of precision agriculture. This technology is designed to enhance management practices by tailoring agricultural treatments to specific production zones within a field, thereby conserving resources, protecting the environment, and improving crop quality. This paper aims to developed spatial modeling for Site-Specific Management Zones based on soil and plant parameters. This work was done in Agrofood Company with an area approximately 18.69 hectares (ha) planted with potato crop (DITTA variety) in winter session 2019. 21 soil samples were taken before planting the crop and 21 plant samples were taken during the three growth stages based on the grid system, Soil and plant variability maps were produced based on Geostatistical Analysis of laboratory analyses of the samples. Spatial Model was developed to delineate site-specific management zones using ArcGIS 10.8, the input factors to developed this model were soil maps for Nitrogen (N), Phosphors (P), Potassium (K), pH, electrical conductivity (ECe), calcium carbonate (CaCO3) and soil organic carbon (SOM), plant maps for NPK, Chlorophyll a, Chlorophyll b, Carotenoids (Car) yield data maps and normalized difference vegetation index (NDVI) to create Site -Specific management zones for the study area. The results showed that management zones were classified into two zones, which zone (I) with an area 10.94 Hectares and its was characterized by high crop yield and good soil parameters, on the other hand zone (II) with an area 7.75 hectares and it was characterized by low crop yield, low soil and plants parameters. [ABSTRACT FROM AUTHOR]

The effectiveness of agroecological management strategies (AEMS) in controlling bacterial wilt caused by Ralstonia solanacearum species complex (RSSC) remains a critical question. A quantitative review was conducted based on publications reporting agroecological bacterial wilt management practices under on‐station or on‐farm field conditions in electronic libraries, without geographical restrictions, on tomato, pepper, eggplant and potato. After applying inclusion and exclusion criteria, 48 papers were retained, resulting in 644 and 146 observations of bacterial wilt incidence (BWI) and yield, respectively. The effectiveness of AEMS for each crop was evaluated based on BWI and crop yield. The effect size was calculated as the natural logarithm of the response ratio, and confidence intervals were determined by bootstrapping. Varietal control, biological control (used of biological control agents) and combinations of many AEMS were the most reported practices, both under on‐station and on‐farm conditions. The use of plant extracts (Thymus spp., Crotalaria spp.), grafting, solarization, soil amendments (manure, compost) and crop rotations (with maize, beans) were also reported. All AEMS significantly reduced bacterial wilt incidence and increased yields compared to the untreated plots. Grafting, combinations of control methods, varietal control and biological control were the most effective agroecological practices in reducing BWI and increasing yield in tomato and eggplant. Varietal control was the most effective compared to biological control on pepper. The effectiveness of AEMS also varied with crop susceptibility and origin, climatic zone and soil acidity. This study highlights the potential of AEMS in reducing BWI and increasing crop yield in infested soils. [ABSTRACT FROM AUTHOR]

Potato virus S (PVS) is a noteworthy threat to the propagation of healthy seed potatoes. Accurate and speedy detection is critical for effective PVS management. In the present study, an isothermal-based one-step reverse transcription-recombinase polymerase amplification (RT-RPA) approach was developed to detect PVS infection in potato leaves and tubers. A primer set based on the coat protein gene successfully amplified a 158 bp product out of three primer sets examined. The amplification reaction took less than 30 min to complete with no account of cross-reactivity with major potato viruses. Additionally, amplification of RT-RPA products was performed on the heating system and/or water bath at 38–42 °C. The results of sensitivity analysis revealed that one-step RT-RPA has shown 100 times higher sensitivity than routine RT-PCR for the detection of PVS in infected leaves. Furthermore, ten times higher sensitivity of RT-RPA was observed in infected tubers. The methodology was simplified further by the use of template RNA extracted using a cellular disc paper-based extraction method that detected the PVS more effectively than purified total RNA. PVS was detected in 175 samples (leaves and tubers each) of several potato varieties using this innovative technique. To our acquaintance, this is the first report of one-step RT-RPA using a basic RNA extract derived through cellular disc paper that is significantly sensitive and precise for PVS detection in potatoes. The advantages of one-step RT-RPA in terms of proficiency, robustness, and the availability of a highly pure RNA template make it an attractive choice for seed accreditation, resistance breeding, and field inspections. • A single step RT-RPA has been developed for the detection of PVS in potato leaves and tubers. • The RPA technique was standardized with simple RNA extract which altogether showed higher sensitivity than other PCR assays. • The method was validated using 175 field samples of infected potato that demonstrated its suitability for large scale use. [ABSTRACT FROM AUTHOR]

It has become necessary to use various techniques in agriculture including nanotechnology since it is gaining interest as one of the promising approaches to increase crop production. It possesses all the characteristics needed to be used in agriculture such as high solubility, good efficiency and can be used in small quantities. So, this directly increases the efficiency of fertilizers and reduces the cost compared to conventional fertilizers. An investigation was carried out in Babylon in the year 2017 using six fertilizer combinations which were as follows : (traditional chemical fertilizer according to recommended quantity is 600 kg/ha solo, add chemical fertilizer quarter recommended dosage+fertilizer nano, chemical fertilizer half recommended dosage+nano-fertilizer, add three-quarter chemical fertilizer dosage of recommended quantity+nano-fertilizer, add full chemical fertilizer recommended dosage+nano-fertilizer, add individual nano-fertilizers). Three planting dates such as 1st September, 10th September and 20th September were checked. The fertilizer combination surpassed the full recommended dosage+nano-fertilizer in the paper area, percentage of dry matter of vegetative total, total number of tubers per plant, total yield, starch percentage and the superiority of the treatment in the third planting date in terms of total number of tubers per plant, total yield and dissolved solids. The 'first planting' date missed in the rate of tuber weight exceeded the overlap between the combination fertilizer i. e. entire recommended dosage+nano-fertilizer. The third planting time excelled in paper space, total quotient and the overlap between the combination fertilizer i. e. entire recommended dosage+nano-fertilizer, whereas in the second planting date it occurred in the percentage of the dry matter of vegetative total and starch percentage. [ABSTRACT FROM AUTHOR]

Continuous improvements in potato harvesting and storage techniques in recent years have reduced external potato damage such as shatter or skinning. However, the internal tuber damage caused by mechanical stress, i.e. blackspot, cannot be sufficiently reduced with conventional development tools such as field trials. Due to the approximately two-day duration of the blackspot development following the mechanical stress, it is offen unclear in which process step and under what environmental conditions the damage occurs. Particle simulation of agricultural harvesting processes offier extensive opportunities to improve machine processes and reduce harvesting losses. Using the Discrete Element Method (DEM), it is possible to gain a deep understanding of the harvesting and transportation processes of potatoes. The DEM represents potato tubers as particles and machine components as geometry elements. The geometry elements can be stationary or follow predefined movements, while the particle motion is calculated based on contact laws and material properties. The global particle model of DEM can predict contact stresses on a particle level, but is unable to describe local damages such as blackspot. This paper introduces a test rig for investigating mechanically induced discoloration of potato tissue and describes a model for representing local blackspot damage in DEM. Due to the inhomogeneous composition of the potato tuber with multiple tissue types, the three primary tissue types: cortex, perimedullary parenchyma and pith tissue are examined separately on the developed test rig. The mechanical stress on the tissue samples is applied up to a maximum compression strain of 20, 40, and 50%, with a defined strain rate ranging from 0.1 to 10000% per second, relative to the sample height. The colour change of the samples is examined affer approximately 48 hours against unloaded reference tissue. The tests were conducted at a tuber and ambient temperature of 14--20°C. The correlation between the colour change and the load parameters, compression strain and strain rate, can be described by an empirical equation. By implementing this equation as a damage model in the DEM contact model, the origination of blackspot in real processing stages can be predicted and investigated in simulations. The damage value of individual particles can be compared with the damage observed in real potatoes, allowing for the validation of the damage model. [ABSTRACT FROM AUTHOR]