Machine learning in agriculture domain: A state-of-art survey

Meshram, Vishal; Patil, Kailas; Meshram, Vidula; Hanchate, Dinesh Bhagwan; Ramkteke, S.D.

doi:10.1016/j.ailsci.2021.100010

Cited by 187 publications

(108 citation statements)

References 46 publications

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“…Because of the increased collection of medical data, practitioners now have a great opportunity to promote healthcare diagnosis. ML plays a vital role in many applications like text detection and recognition [ 3 ], early prediction [ 4 ], power quality disturbance detection [ 5 ], truck traffic classification [ 6 ], and agriculture [ 7 ]. ML has now become an essential tool in the healthcare sector to aid with patient diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

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Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.

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Section: Introductionmentioning

confidence: 99%

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick¹,

Aruna

Samikannu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The profit percentage share of fruit market is substantial with respect to the total agriculture output [1] , [2] , [3] . In the agro-industry fast and accurate fruit classification is the highest need.…”

Section: Data Descriptionmentioning

confidence: 99%

FruitNet: Indian fruits image dataset with quality for machine learning applications

Meshram¹,

Patil²

2022

Data in Brief

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Fast and precise fruit classification or recognition as per quality parameter is the unmet need of agriculture business. This is an open research problem, which always attracts researchers. Machine learning and deep learning techniques have shown very promising results for the classification and object detection problems. Neat and clean dataset is the elementary requirement to build accurate and robust machine learning models for the real-time environment. With this objective we have created an image dataset of Indian fruits with quality parameter which are highly consumed or exported. Accordingly, we have considered six fruits namely apple, banana, guava, lime, orange, and pomegranate to create a dataset. The dataset is divided into three folders (1) Good quality fruits (2) Bad quality fruits, and (3) Mixed quality fruits each consists of six fruits subfolders. Total 19,500+ images in the processed format are available in the dataset. We strongly believe that the proposed dataset is very helpful for training, testing and validation of fruit classification or reorganization machine leaning model.

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“…While van Klompenburg et al, (2020) conducted a systematic review on the application of machine learning in crop yield prediction. Meshram et al, (2021) in their published article titled "Machine learning in agriculture domain: A state-of-art survey" they did an extensive survey of the latest machine learning application in agriculture they focused on pre-harvesting, harvesting, and post-harvesting. Consequent to the reviewed literature, our work provides an updated systematic review on the subject and considers a broader aspect of its application in agriculture.…”

Section: Related Workmentioning

confidence: 99%

An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria

Umar¹,

Sani²,

Suleiman³

2022

SLUJST

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Globally agriculture has remained a key factor in food security, employment, and several other favorable economic indices. However, factors like rising world population, trade globalization, and climate variabilities have created the need for modernization and optimization to boost production and livelihood. Machine learning allows machines to read from a pool of available data and provide data-centric results. This has opened up a new and promising perspective. The paper examines recent proven works in machine learning technology application in agriculture to establish the modest contribution of machine learning and emerging deep learning technologies in this field to highlight the need for its adoption in the Nigerian agricultural ecosystem. Therefore, a systematic review was carried out using a categorization model of key agricultural subsectors/activities. Findings have shown a widespread of its application with significant positive impact in almost every aspect of agriculture with new works showing higher result efficiency in deep learning technologies application. Insightful recommendation from these technologies has proven capable of boosting agriculture on various fronts. Thus, the adoption of ML/DL technologies in Nigeria’s Agriculture will go a long way in helping the country attain food sufficiency.

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Machine learning in agriculture domain: A state-of-art survey

Cited by 187 publications

References 46 publications

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

FruitNet: Indian fruits image dataset with quality for machine learning applications

An Overview of Machine and Deep Learning Technologies Application in Agriculture: Opportunities and Challenges in Nigeria

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