Crop Recommender System Using Machine Learning Approach

Pande, Shantanu; Ramesh, P.; Anmol, Anmol; Aishwarya, B R; Rohilla, Karuna; Shaurya, Kumar

doi:10.1109/iccmc51019.2021.9418351

Cited by 112 publications

(16 citation statements)

References 12 publications

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“…The model has certain limitations in dealing with forecasting models. Reference 14 have proposed a recommender system using classification techniques like SVM, ANN, RF, MLR, and KNN. The model was evaluated over the maize, soybean, wheat, and paddy across divergent soil types, and the models obtained reasonable accuracies, where the ANN with 86%, SVM with 75%, MLR with 60%, KNN with 90%, and RF with 95%.…”

Section: Literature Reviewmentioning

confidence: 99%

XAI‐driven model for crop recommender system for use in precision agriculture

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

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Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1‐score. In contrast to other cutting‐edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1‐score by 10%.

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Section: Literature Reviewmentioning

confidence: 99%

XAI‐driven model for crop recommender system for use in precision agriculture

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

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“…Doshi et al [11] proposed a smart technology named AgroConsultant that aim is to help the Indian farmer in making an informed decision where crop grows dependent on geographical position, soil characteristics, the sowing season, and environmental factors like rainfall and temperature. Pande et al [12] presented a user-friendly and yield viable prediction technique for the farmer. The presented method offers connectivity to farmers through mobile applications.…”

Section: Related Workmentioning

confidence: 99%

“…The value of parameters a 1 and a 2 are set to two and one correspondingly. Afterward putting the expression provided in (12) into Eq. ( 10), the attained value of Fi is provided as:…”

Section: Crop Recommendation Modulementioning

confidence: 99%

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

Gopi¹,

Karthikeyan²

2023

Intelligent Automation &Amp; Soft Computing

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Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction. At the initial stage, equilibrium optimizer (EO) with kernel extreme learning machine (KELM) technique is employed for effectual recommendation of crops. Next, random forest (RF) technique was executed for predicting the crop yield accurately. For reporting the improved performance of the MMML-CRYP system, a wide range of simulations were carried out and the results are investigated using benchmark dataset. Experimentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

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“…The authors of [16] presented a mobile application for a user who inputs soil type and area as input and estimates crop yield per hectare for the states of Karnataka and Maharashtra. In addition, the model predicts agricultural output per hectare for a chosen crop.…”

Section: Related Workmentioning

confidence: 99%

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Kumar¹,

Lahza²,

Sreenivasa³

et al. 2023

Computer Systems Science and Engineering

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Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.

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Crop Recommender System Using Machine Learning Approach

Cited by 112 publications

References 12 publications

XAI‐driven model for crop recommender system for use in precision agriculture

XAI‐driven model for crop recommender system for use in precision agriculture

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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