Survey of unsupervised machine learning algorithms on precision agricultural data

Mehta, Parth; Shah, Hetasha; Kori, Vineet; Vikani, Vivek; Shukla, Soumya; Shenoy, Mihir

doi:10.1109/iciiecs.2015.7193070

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…Clustering is an unsupervised machine learning technique (Haroon, 2017; Louridas and Ebert, 2016; Sotiropoulos and Tsihrintzis, 2017), which means, unlike supervised machine learning technique, it is not allowed to validate predicted output due to unlabeled data (Ozgur, 2004). Many clustering techniques are used in practice such as k-means, DBSCAN, Agglomerative, Divisive (DIANA) or COBWEB (Mehta et al , 2015). In this article, k-means clustering is used to cluster countries based on the similarities among the influencing factors.…”

Section: Methodsmentioning

confidence: 99%

Using k-means clustering in international location decision

Khalid

Hansen

2018

JGOSS

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Purpose This article investigates the application of unsupervised machine learning in the international location decision (ILD). This paper addresses the need for a fast, quantitative, and dynamic location decision framework. Methodology/Approach Unsupervised machine learning technique i.e. k-means clustering is used to carry out the analysis. 24 different indicators of 94 countries, categorized into five groups, have been used in the analysis. After the clustering, the clusters have been compared and scored to select the feasible countries. Findings A new framework is developed based on k-means clustering that can be used in ILD. This method provides a quantitative output without personal subjectivity. The indicators can be easily added or extracted based on the preferences of the decision makers. Hence, it was found out that the unsupervised machine learning i.e. k-means clustering is a fast and flexible decision support framework that can be used in ILD. Limitations Limitations include the generality of selected indicators and clustering algorithm used. The use of other methods and parameters may lead to alternate results. Originality/Value The framework developed through the research intends to assist the decision makers in deciding on the facility locations. The framework can be used in international as well national domains. It provides a quantitative, fast, and flexible way to shortlist the potential locations. Other methods can also be used to further decide on the specific location.

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Section: Methodsmentioning

confidence: 99%

Using k-means clustering in international location decision

Khalid

Hansen

2018

JGOSS

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“…The authors of [28] proposed a survey of public datasets that can be used for precision agriculture. The authors of [29] proposed a survey of unsupervised ML techniques for precision agriculture. The authors of [30] proposed a survey of supervised ML classifiers for plant disease detection.…”

Section: Related Workmentioning

confidence: 99%

Soybean cyst nematode detection and management: a review

et al. 2022

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Soybeans play a key role in global food security. U.S. soybean yields, which comprise $$32\%$$ 32 % of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods—adopted by most of the laboratories to make recommendations—is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN’s increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

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“…This information will be sent as an SMS to the farmers. The parameters include soil temperature, atmospheric temperature, and humidity [3]. This system suggests further improving the effectiveness by predicting the suitable time for applying pesticides, fertilizer, and manures [4].…”

Section: Related Workmentioning

confidence: 99%

Crop Recommendation on Analyzing Soil Using Machine Learning

al.¹

2021

TURCOMAT

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Agriculture plays a significant role in increasing the economic development of our nation. Crop production has greatly affected due to changes in weather pattern. Emerging technologies can be used to improve productivity of the crops by converting traditional farming to precision farming. The new technologies that are used include data analysis and Internet of things (IOT). The major issue yet to be resolved is cultivating precise crop at precise time. This can be done with the help machine learning algorithms which is found to be an effective method for predicting the suitable crop. The soil parameters such as soil moisture, temperature, humidity and pH are collected from the sensors using IOT and given to Graphical User Interface (GUI). GUI gets the inputs and suggests the suitable crops. The system developed using IOT and ML greatly helps the farmers to take a valuable decision.

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Survey of unsupervised machine learning algorithms on precision agricultural data

Cited by 13 publications

References 37 publications

Using k-means clustering in international location decision

Using k-means clustering in international location decision

Soybean cyst nematode detection and management: a review

Crop Recommendation on Analyzing Soil Using Machine Learning

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