Soil Analysis and Crop Recommendation using Machine Learning

Motwani, Aditya; Patil, Param; Nagaria, Vatsa; Verma, S. S.; Ghane, Sunil

doi:10.1109/iconat53423.2022.9725901

Cited by 25 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The notation 𝜎, whose values are always greater than 0, is associated with neuron bandwidth. Similarly, the first hidden layers will contribute to the second hidden layer across the weight coefficients, as shown in Equations ( 7) and (8).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

View full text Add to dashboard Cite

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%.

show abstract

Section: Methodsmentioning

confidence: 99%

“…According to research conducted by Motwani et al, 8 a crop recommender system was developed using soil analysis and deep learning techniques, namely the convolutional neural network (CNN). The study reports that the CNN model achieved an accuracy rate of 95.21%, while the RF model achieved an accuracy rate of 75%.…”

Section: Introductionmentioning

confidence: 99%

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

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…al. [10] proposed a model which worked on the same dataset mentioned in the paper below. There are four various kinds of soil in the dataset.…”

Section: Background and Related Workmentioning

confidence: 99%

Visual Transformer for Soil Classification

Jagetia

Goenka

Kumari

et al. 2022

2022 IEEE Students Conference on Engineering and Systems (SCES)

View full text Add to dashboard Cite

Our food security is built on the foundation of soil. Farmers would be unable to feed us with fiber, food, and fuel if the soils were not healthy. Accurately predicting the type of soil helps in planning the usage of the soil and thus increasing productivity. This research employs state-of-theart Visual Transformers and also compares performance with different models such as SVM, Alexnet, Resnet, and CNN. Furthermore, this study also focuses on differentiating different Visual Transformers architectures. For the classification of soil type, the dataset consists of 4 different types of soil samples such as alluvial, red, black, and clay. The Visual Transformer model outperforms other models in terms of both test and train accuracies by attaining 98.13% on training and 93.62% while testing. The performance of the Visual Transformer exceeds the performance of other models by at least 2%. Hence, the novel Visual Transformers can be used for Computer Vision tasks including Soil Classification.

show abstract

“…to improve the accuracy of the framework that generates a list of suitable crops based on the soil type. Another author [4] (CNN) with symmetrical architectures gave significantly better results for image classification for the selected soil classification dataset. this system is adapted to a mixture of soil types, by accounting for the composition present in the minority of the mixture.…”

Section: Literature Surveymentioning

confidence: 99%

Study of Revolutionary use of Machine Learning and IoT based Model in Agricultural Sector: A Review

Kharde¹,

Mahajan²,

Galande³

et al. 2022

IJETMS

View full text Add to dashboard Cite

In traditional ways, understanding the situation of the crop is not that much easy to detect and prevent the diseases in the crop. The availability of skill full farmers and manpower is less. Farmers still have insufficient knowledge and awareness of available latest technologies to facilitate fast and effective management of crops, parameters like soil health, effect of weather condition, water management, species management, yield prediction, crop recommendation, use of fertilizers etc. Now a days Machine learning model is widely used to predict the accuracy of the result. This article aims to provide information of different machine learning approaches proposed to study soil health and crop predictions with its advantages in agriculture sector of India in last decade.

show abstract

Soil Analysis and Crop Recommendation using Machine Learning

Cited by 25 publications

References 9 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

Visual Transformer for Soil Classification

Study of Revolutionary use of Machine Learning and IoT based Model in Agricultural Sector: A Review

Contact Info

Product

Resources

About