Potato Research最新文献

{"title":"Potato Leaf Disease Classification Using Optimized Machine Learning Models and Feature Selection Techniques","authors":"Marwa Radwan, Amel Ali Alhussan, Abdelhameed Ibrahim, Sayed M. Tawfeek","doi":"10.1007/s11540-024-09763-8","DOIUrl":"https://doi.org/10.1007/s11540-024-09763-8","url":null,"abstract":"<p>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 <i>K</i>-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 <i>K</i>-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.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil Tillage, Straw Mulching, and Microalgae Biofertilization in Potato Production in Conventional and Organic Systems","authors":"Renato Yagi, Emanuelle C. Dobrychtop, Henrique v. H. Bittencourt, Diva S. Andrade, Jackson Kawakami, Rogério P. Soratto","doi":"10.1007/s11540-024-09765-6","DOIUrl":"https://doi.org/10.1007/s11540-024-09765-6","url":null,"abstract":"<p>This study explores soil and fertilizer management techniques using winter cereal rye and <i>Chlorella sorokiniana</i> 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 <i>La Niña</i> 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.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elevated Carbon Dioxide only Partly Alleviates the Negative Effects of Elevated Temperature on Potato Growth and Tuber Yield","authors":"S. C. Kiongo, N. J. Taylor, A. C. Franke, J. M. Steyn","doi":"10.1007/s11540-024-09767-4","DOIUrl":"https://doi.org/10.1007/s11540-024-09767-4","url":null,"abstract":"<p>The current rapid increase in ambient carbon dioxide concentration ([CO₂]) and global temperatures have major impacts on the growth and yield of crops. Potato is classified as a heat-sensitive temperate crop and its growth and yield are expected to be negatively affected by rising temperatures, but it is also expected to respond positively to increasing ambient [CO₂]. In this study, we investigated the physiological, growth, and yield responses of two potato cultivars to elevated temperature (eT) and the possible role of elevated [CO₂] (e[CO₂]) in counteracting the negative effects of eT. Two growth chamber trials (trials 1 and 2) were conducted using two temperature regimes: ambient temperature (aT, <i>T</i>_min/<i>T</i>_max = 12/25 ℃) and eT (<i>T</i>_min/<i>T</i>_max = 15/38 ℃), and two [CO₂]: ambient (a[CO₂]) = 415 ppm and e[CO₂] = 700 ppm. Temperatures gradually rose from the minimum at 6.00 AM to reach <i>T</i>_max at noon, then <i>T</i>_max was maintained for 1 h in trial 1 and for 4 h in trial 2. Elevated [CO₂] increased photosynthesis (<i>Anet</i>) in both cultivars at aT and eT. Elevated temperature also stimulated <i>Anet</i> compared to aT. Elevated [CO₂] significantly reduced stomatal opening size, while eT resulted in larger stomata openings and higher stomatal conductance. Elevated [CO₂] increased tuber yields at aT in both trials. Tuberisation was delayed by eT in trial 1, and completely inhibited in trial 2 even at e[CO₂], resulting in no tuber yield. The two cultivars responded similarly to treatments, but Mondial initiated more tubers and had higher tuber yield than BP1. The results suggest that potato will benefit from e[CO₂] in future, even when exposed to high <i>T</i>_max for a short period of the day, but the benefit will be eroded when the crop is exposed to high <i>T</i>_max for an extended period of the day.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Potassium Fertilizer Base/Topdressing Ratio on Dry Matter Quality, Photosynthetic Fluorescence Characteristics and Carbon and Nitrogen Metabolism of Potato","authors":"Jiali Xie, Ming Li, Mingfu Shi, Yichen Kang, Ruyan Zhang, Yong Wang, Weina Zhang, Shuhao Qin","doi":"10.1007/s11540-024-09757-6","DOIUrl":"https://doi.org/10.1007/s11540-024-09757-6","url":null,"abstract":"<p>Potassium is an essential nutrient element for potato production. However, there is little research on how the base/topdressing ratio of potassium fertilizer affects plant growth. Therefore, in this 2-year (2022–2023) study, we used Longshu 7 as the experimental material and conducted a pot experiment. Under the condition of total potassium application of 5.4 g/plant, the potassium fertilizer base/topdressing ratios were as follows: CK (10:0), T1 (2:8), T2 (4:6), T3 (6:4), and T4 (8:2). We investigated the effects of potassium fertilizer application on dry matter quality, endogenous hormones, photosynthetic fluorescence characteristics, carbon and nitrogen metabolism and yield in potato. The results of the study demonstrated that potassium topdressing had a positive effect on plant growth through the optimization of endogenous hormone content and regulation of cell elongation. In addition, potassium application can enhance the activity of enzymes related to carbon and nitrogen metabolism, promote photosynthesis, improve the transport efficiency of photosynthetic products and enhance the dry matter quality of tubers. Among all the potassium topdressing treatments, the T2 treatment exhibited a significant difference. However, it is important to note that an excessive increase in the base/topdressing ratio of potassium fertilizer may have detrimental effects on the levels of gibberellin A3 (GA₃) and starch content. Based on Pearson correlation analysis, it was determined that the activities of sucrose synthase (SuSy), sucrose phosphate synthase (SPS) and glutamine synthetase (GS) play a significant role in influencing the dry matter quality of potato tubers. These findings provide valuable insights into the importance of these factors in potato production. Overall, the results of this study highlight the significance of maintaining an appropriate ratio of base to topdressing of potassium fertilizer. This optimal ratio ensures the efficient assimilation and utilization of nitrogen and carbon, ultimately serving as a valuable theoretical foundation for effective potassium fertilizer application in potato production.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potato Yield Classification Using Weather Variables: a Discriminant Analysis Approach","authors":"S. R. Krishna Priya, N. Naranammal, Walid Emam, Y. Tashkandy, Monika Devi, Pradeep Mishra","doi":"10.1007/s11540-024-09761-w","DOIUrl":"https://doi.org/10.1007/s11540-024-09761-w","url":null,"abstract":"","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141644672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model","authors":"Marwa Eed, A. Alhussan, Al-Seyday T. Qenawy, Ahmed M. Osman, Ahmed M. Elshewey, Reham Arnous","doi":"10.1007/s11540-024-09764-7","DOIUrl":"https://doi.org/10.1007/s11540-024-09764-7","url":null,"abstract":"","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture","authors":"El-Sayed M. El-Kenawy, Amel Ali Alhussan, Nima Khodadadi, Seyedali Mirjalili, Marwa M. Eid","doi":"10.1007/s11540-024-09753-w","DOIUrl":"https://doi.org/10.1007/s11540-024-09753-w","url":null,"abstract":"<p>Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an <i>R</i>² of 0.49168, while XGBoost had an MSE of 0.03583 and an <i>R</i>² of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an <i>R</i>² of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of Resistance Levels of National Potato Cultivars and Clones Against Golden Cyst Nematode Pathotype Ro2/3 via Phenotypic and DNA Marker-Assisted Characterization","authors":"Gülten Kaçar Avcı, Ramazan Canhilal, Halil Toktay, Mustafa İmren, Levent Ünlenen, Uğur Pırlak","doi":"10.1007/s11540-024-09750-z","DOIUrl":"https://doi.org/10.1007/s11540-024-09750-z","url":null,"abstract":"<p>Potato (<i>Solanum tuberosum</i> L.) is one of our important agricultural products, which is the main food source for people in Türkiye, as well as all over the world. There are many diseases and pests that reduce productivity in potato plant production. Potato cyst nematodes (Tylenchida: Heteroderidae) are pests that are on the quarantine list of the European and Mediterranean Plant Protection Organization and cause serious yield losses. Since they are soil-borne pathogens and there is no effective chemical control, the most successful control method is to use resistant cultivars. The aim of the study was to determine the resistance levels of local and national potato cultivars and clones developed by the Nigde Potato Research Institute against the <i>Globodera rostochiensis</i> Ro2/3 pathotype using molecular marker analysis and biotesting methods. The biotest study was carried out by inoculating 7500 eggs and larvae of the <i>Globedera rostochiensis</i> pathotype Ro2/3 into pots. In the molecular marker analysis, resistance was investigated with TG689, 57R, Gro1-4 markers. While all cultivars and clones except Bettina were grouped as sensitive in the biotesting study, the <i>H1</i> resistance gene was detected in Onaran, Ünlenen, Leventbey, Muratbey, Nahita, Agria, Madeleine, Desiree and Bettina cultivars by molecular marker analysis. <i>H1</i> and <i>Gro1-4</i> resistance genes were detected in the PAE 13–08-07, PAE 13–08-08 and PAE 13–08-14 clones used in the experiment. The results showed that clones developed by the Potato Research Institute exhibited highly resistant marker alleles for the Ro2/3 pathotype of <i>G. rostochiensis</i>. The results of phenotyping study and the molecular marker study were not similar.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141609935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Iron Content in Potatoes: a Critical Strategy for Combating Nutritional Deficiencies","authors":"Zain Mushtaq, Abdulrahman Alasmari, Cihan Demir, Mükerrem Atalay Oral, Korkmaz Bellitürk, Mehmet Fırat Baran","doi":"10.1007/s11540-024-09758-5","DOIUrl":"https://doi.org/10.1007/s11540-024-09758-5","url":null,"abstract":"<p>Despite recent advances in the prevention and control of nutritional deficiencies, estimates suggest that over two billion individuals worldwide are at risk for vitamin A, iodine and/or iron insufficiency. Pregnant women and small children are most at risk, and Southeast Asia and sub-Saharan Africa have very high incidence rates. Concerning public health are deficits in zinc, folate and the B vitamins, among other micronutrients. Micronutrient malnutrition, often referred to as hidden hunger, represents one of humanity’s most pressing challenges. Iron deficiency anaemia affects more individuals globally than any other prevalent disorder. However, iron supplementation can exacerbate infectious diseases, necessitating careful evaluation of iron therapy policies. In this review, we explore biofortification strategies to combat hidden hunger, considering recent medical and nutritional advancements. Enhancing iron content in edible plant parts can improve human nutrient status through crop consumption. Mineral and vitamin density in staple foods, particularly for impoverished populations, can be increased using traditional plant breeding or transgenic approaches, collectively known as biofortification. Microbial iron biofortification is especially valuable in developing countries where expensive supplements are unaffordable. Additionally, the current COVID-19 pandemic underscores the need for a robust immune system, with iron playing a crucial role in immune function enhancement.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Detection of Potato Disease Using an Enhanced Convolutional Neural Network-Long Short-Term Memory Deep Learning Model","authors":"Sarah A. Alzakari, Amel Ali Alhussan, Al-Seyday T. Qenawy, Ahmed M. Elshewey","doi":"10.1007/s11540-024-09760-x","DOIUrl":"https://doi.org/10.1007/s11540-024-09760-x","url":null,"abstract":"<p>Potato diseases pose a significant threat to farmers, impacting potato crops’ productivity, quality, and financial stability. Among the most notorious diseases is late blight, caused by <i>Phytophthora infestans</i>, famously responsible for triggering the Irish Potato Famine in the 1840s. Late blight swiftly devastates potato foliage and tubers, particularly in damp, humid conditions. Another common disease is early blight, attributed to <i>Alternaria solani</i>. This disease affects various parts of the potato plant—leaves, stems, and tubers. It mainly shows up in the form of dark stains around the center of a bull’s eye on the leaves, bringing down both the yield and the crop quality. A model consisting of a Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) enhanced for potato disease detection was proposed in our paper. The dataset used was Z-score standardized before the training and testing process using the proposed CNN-LSTM model was started. The performance of the implemented model, CNN-LSTM, was analyzed alongside five traditional machine learning algorithms, namely Random Forest (RF), Extra Trees (ET), K-Nearest Neighbours (KNN), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM). Accuracy, sensitivity, specificity, F-score, and AUC were the metrics included in the evaluation, confirming the effectiveness of the models. The results of the experiments showed that our CNN-LSTM reached the highest accuracy at 97.1%.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}