Crop Recommendation and its Optimal Pricing using ShopBot

Chaudhari, Apoorva; Beldar, Manasi; Dichwalkar, Riya; Dholay, Surekha

doi:10.1109/icosec49089.2020.9215411

Cited by 13 publications

(3 citation statements)

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“…The study highlights the potential of using machine learning tools and techniques to optimize agricultural practices and improve crop management. Apoorva Chaudhari et al [6] employed data mining techniques and web scraping to create a platform for crop suggestion and optimal pricing, aiming to benefit farmers by providing recommendations for crop selection and optimal purchasing of crop seeds.…”

Section: Literature Surveymentioning

confidence: 99%

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Kakade,

Kulkarni,

Dhawale

et al. 2023

Green Intell. Syst. Appl.

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Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

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Section: Literature Surveymentioning

confidence: 99%

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Kakade,

Kulkarni,

Dhawale

et al. 2023

Green Intell. Syst. Appl.

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“…Chaudhari, A. et al [2] in this paper proposed a platform for crop recommendation and its optimal seed price using shopbot. A dataset with the following parameters -crop, rainfall, temperature, season, year, production, area, and the location used for crop recommendation.…”

Section: Literature Surveymentioning

confidence: 99%

“…Agriculture is the prime source of food production and also supplies raw materials to various industries. Population growth drives up food demand [2]. Farming needs to be done in an effective way to increase food production.…”

Section: Introductionmentioning

confidence: 99%

Krashignyan: A Farmer Support System

Kanchan¹,

Shardoor²

2021

AJCT

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Agriculture is the primary component of the Indian economy. It is the primary source of food supply and is essential to our livelihoods. The majority of Indians rely on agriculture for their employment. Agriculture production declines as a result of unpredictable weather, wrong selection of crops, unbalanced fertilizer use, and a lack of market awareness. Farmers face numerous challenges in traditional farming, and many times, farmers fail to select the appropriate crop for cultivation. Crop growth is affected by a variety of factors such as weather, soil parameters, and fertilizers. A crop recommendation system is proposed in this paper to assist farmers in selecting the appropriate crop based on the location, weather data, crop sowing season, and soil parameter. Various Machine Learning techniques, such as Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes, and XGBoost Classifier methods, were used for recommendation.The XGBoost classifier gives the best results with a 97% accuracy, hence the final model was developed using the XGBoost classifier. This system will help farmers in selecting the best crop for their fields while increasing agricultural yield.

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