Intelligent Crop Recommendation System using Machine Learning

Priyadharshini, A.; Chakraborty, Swapneel; Kumar, Aayush; Pooniwala, Omen Rajendra

doi:10.1109/iccmc51019.2021.9418375

Cited by 62 publications

(10 citation statements)

References 9 publications

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“…. [14] explains about, Agriculture is the backbone of our country, Over 58% of people in the world make their living from agriculture. But farmers are failing to pick the right crop to harvest because they use old, traditional methods.…”

Section: Literature Surveymentioning

confidence: 99%

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Literature Review of IOT and ML Applications in Agriculture

Annapoorna¹,

Adahalli²,

Abhishek³

et al. 2023

IJSREM

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Agriculture is the backbone of the economy. It provides for more than just the necessities of life for people; in underdeveloped nations, it also provides work. There is a reduction in the crop yield and quality. This leads to scarcity of food. The improved technologies like Machine Learning (ML), and the Internet of Things (IOT) are applied in agriculture to overcome the challenges in agriculture. IOT devices like sensors are used in the field to collect soil parameters. Based on sensor-collected data, the ML model recommends the suitable crop for cultivation, which gives good yield and profit to the farmers. Hybrid algorithms are used to perform analysis over the dataset and provide results. In this survey, we examine the previous research work various authors have carried out on implementing IOT and ML in several agricultural principles. Keywords— Internet of Things (IoT), Machine Learning (ML)

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Section: Literature Surveymentioning

confidence: 99%

“…then choosing a crop is a challenge. [27], has given solution for this by proposing a method called crop selection method (CSM) to solve the crop selection problem. Publishers have studied the different subsets of crop influencing factors by different prediction models.…”

Section: Literature Surveymentioning

confidence: 99%

Literature Review of IOT and ML Applications in Agriculture

Annapoorna¹,

Adahalli²,

Abhishek³

et al. 2023

IJSREM

View full text Add to dashboard Cite

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“…The paper [1], [2], and [3] mainly throw light on the recommendation of the crops that which crop to grow according to the soil's nutrition value and the weather conditions. In this paper, the author starts from the very basics of smart farming and further moves towards developing a model which will help the farmer to grow crops that are suitable according to the soil along with the weather conditions.…”

Section: Literature Surveymentioning

confidence: 99%

“…Another severe issued faced is when a plant gets caught by heterogeneous diseases that has a drastic effect on agriculture sector such as less amount of agriculture production. To overcome all these issues this recommendation has been proposed [1]. Crop recommendation is characterized by a soil database comprised of Nitrogen, Phosphorus, potassium in addition to climate conditions such as rainfall and Ph level.…”

Section: Introductionmentioning

confidence: 99%

Kisan Mitra-Initiative for Farmers

2023

IRJMETS

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In India, the farming sector is highly crucial for the economic growth of the country. In India, agriculture provides a living for approximately 51% of the people. Agriculture provides the various opportunities for the villagers to work and contribute widely to the development of our country on a very huge scale, as well as it gives a massive boost to the economy. The research aims to assist the farmers in determining the quality of the soil and assessing its many parameters, as well as recommending crops and fertilizers depending on the outcomes acquired through machine learning technique. To improve the effectiveness of the Crop Recommendations Systems and Fertilizer Recommendation System, the system employs a number of Classification techniques. The assigned soil and fieldwork to anticipate a list of crops that is suited for the soil, as well as the knowledge on minerals that are insufficient in the soil. As a result, the user is free to choose which crop to plant. As a result, the approach aids farmers in gaining information. This research uses soil and PH data as inputs and uses a website to forecast which crops are suited for the soil and which fertilizers can be used as a remedy. The research also aims to assist the farmers by determining the disease occurring in the plant where the user just needs to click the picture of the leaf of the plant which is having the disease and the system predicts that what type of disease it is and what are the organic techniques to mitigate that plant disease and save the remaining crops.

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“…Several studies have used machine learning approaches to assess yield prediction for crops, such as mustard and potato, by examining soil properties and using agronomic algorithms. Support vector machine (SVM) outperformed the decision tree method in terms of accuracy, with a score of 92%, and crop recommendations were made to the user using various algorithms, such as K Nearest Neighbor, Decision Tree, Linear Regression model, Support Vector Machine, Naive Bayes, and Neural Network algorithms [19,20]. However, using only two models will not provide the necessary results, as the production value predicted by these algorithms was under 90%.…”

Section: Related Workmentioning

confidence: 99%

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Bilal,

Liu,

Long

et al. 2023

Computers, Materials &Amp; Continua

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Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country's economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learningbased model for plant seedlings, pests, and plant leaf disease datasets is used to detect weeds, pesticides, and diseases in the crop. The proposed model achieved an average accuracy of 95%, 97%, and 98% in plant seedlings, pests, and plant leaf disease detection, respectively. The system can help farmers pinpoint the precise measures required at the right time to increase yields.

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Intelligent Crop Recommendation System using Machine Learning

Cited by 62 publications

References 9 publications

Literature Review of IOT and ML Applications in Agriculture

Literature Review of IOT and ML Applications in Agriculture

Kisan Mitra-Initiative for Farmers

Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

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