Crop Type Prediction: A Statistical and Machine Learning Approach

Bhuyan, Bikram Pratim; Tomar, Ravi; Singh, Thipendra P.; Ramdane-Chérif, Amar

doi:10.3390/su15010481

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…The limitation of this method was the high occurrence of errors at different stages of crop growth or water stress. Vij et al [22] recommended an efficient technique for the Internet of Things. To monitor the irrigation system, the farmers required a cheaper and more precise solution.…”

Section: Related Workmentioning

confidence: 99%

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

Munaganuri,

Rao

2023

Environ. Res. Commun.

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In this technological era, various agriculture techniques are used to predict crop growth and crop recommendations. Some of the approaches depend on the IoT smart agricultural-devices to gather information from surrounding. However, multiple information from the sensor and IoT smart device does not provide a clear vision of data based on the agricultural land. Therefore, the proposed model uses various techniques for crop recommendation and crop water forecast requirement. This proposed model effectively predicts crop water requirements and recommendations for suitable crops. The steps involved in crop water requirement prediction are data pre-processing, feature extraction, feature selection, water requirement prediction and recommendation. Initially, the collected IoT data are pre-processed using data normalization, missing value imputation and one-hot encoding. From the pre-processed data, the features are extracted using the Gated Residual autoencoder (GRA) model, whereas the optimal features are selected using the Chaotic Northern Goshawk Optimization (ChaNgo) algorithm. Based on the farmland details, the crop water requirement prediction and suitable crop recommendation based on the market price are carried out using a novel hybrid deep learning model called Channel capsule-assisted stacked dilated Bi-LSTM (Cap-DiBiL). The channel capsule network predicts the crop water requirement and Stacked dilated Bi-LSTM is used for suitable crop recommendations. Then the proposed model analyses the performance and compares it with several existing techniques to prove the proposed model’s enhancement. The proposed model improves the accuracy of predicting the required water and crop recommendations by 98.18%.

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Section: Related Workmentioning

confidence: 99%

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

Munaganuri,

Rao

2023

Environ. Res. Commun.

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“…The ability to accurately forecast the crop types is highly necessary for estimating cultivated area, predicting yield volume, and determining crop water requirements [7]. Detailed Monitoring of agricultural lands is pivotal in precision agriculture, contributing to enhancing crop production and water conservation [8].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Andrade,

Montenegro,

Silva Neto

et al. 2024

AgriEngineering

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Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.

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“…Factors like humidity, temperature, etc., were considered, and machine learning (ML) algorithms were applied-which recommended a suitable crop. Further, Bhuyan et al [4] provided a statistical look at the features, and they indicated the best crop type based on the given features. ML algorithms, like the k-nearest neighbor, support vector machine, random forest, and gradient-boosting trees, have been examined for crop-type prediction.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Crop Recommendation System for Precision Farming in IoT Environment

Patel,

Shah,

Gupta

et al. 2023

Agronomy

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In agriculture, soil is a vital element that decides the quality and yield of agricultural produce. Soil consists of various nutrients such as nitrogen (N), phosphorous (P), potassium (K), the potential of hydrogen (pH), and water content. Nitrogen is responsible for building chlorophyll, which helps produce proteins and thus directly contributes to plant growth and development. Phosphorous is needed to develop root systems and flowers, whereas potassium helps increase disease resistance. Each of these play a role in crop cultivation. Thus, in this research paper, considering the fact that soil health will provide farmers with the best selection of crops that are compatible with their farm’s soil nutrients, we propose an algorithm for recommending a set of suitable crops based on various soil attributes. These soil nutrients can be collected in real-time using soil sensors, such as N, P, K, and pH, and humidity sensors. They can be deployed in farms where the cultivation takes place. These sensor readings would then be transferred to the blockchain layer, thereby validating the data and ensuring it is tamper-proof and evident. The crop recommendation model uses data from these sensors in real-time, increasing the results’ accuracy. The last stage leads us to display these results via a user dashboard, which helps the farmers to keep in check with their farm’s practices, and their sensor states from remote locations.

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Crop Type Prediction: A Statistical and Machine Learning Approach

Cited by 10 publications

References 46 publications

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

Cap-DiBiL: an automated model for crop water requirement prediction and suitable crop recommendation in agriculture

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Blockchain-Based Crop Recommendation System for Precision Farming in IoT Environment

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