A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prabavathi, R.; Chelliah, Balika J.

doi:10.13189/ujar.2022.100310

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The review examines machine learning for yield prediction based on soil nutrients in agriculture, focusing on its e cacy and challenges. It highlights ML's potential to revolutionize yield forecasting by leveraging soil nutrient data for improved agricultural productivity [21].…”

Section: The Study Compared Extreme Learning Machine (Elm) With Multi...mentioning

confidence: 99%

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Asif,

Wahid

2024

Preprint

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This study investigates how machine learning (ML) algorithms can be used in agriculture to forecast soil fertility and maximize crop yield. Machine learning (ML) models are created to predict soil nutrient levels, pH, and organic matter content across a range of geographical locations and land-use types with high accuracy by evaluating large datasets that include soil samples, environmental conditions, and agronomic methods. The research shows the advantages of nonlinear modeling approaches in capturing complex interactions inherent in agricultural systems through a comprehensive evaluation of several machine learning techniques, including ensemble methods like AdaBoost and Extra Tree Classifier. Furthermore, immediate insights and recommendations for improving agronomic decisions are made possible by the integration of real-time sensing technologies, such as proximate sensing, distant sensing, and Internet of Things (IoT) devices. Overall, this work highlights how machine learning (ML) can completely change crop management techniques and soil fertility prediction, enabling a more resilient and sustainable agriculture sector.

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Section: The Study Compared Extreme Learning Machine (Elm) With Multi...mentioning

confidence: 99%

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Asif,

Wahid

2024

Preprint

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“…Various researchers have attempted to improve the crop productivity using tools and techniques of AI. For maize crop, Prabavathi and Chelliah [20] used ML algorithms to predict soil fertility, crop selection and yield rate. The crop prediction performance analysis was seen to be high using decision tree method.…”

Section: Literature Reviewmentioning

confidence: 99%

Sugarcane Disease Detection Using CNN-Deep Learning Method: An Indian Perspective

Upadhye¹,

Dhanvijay²,

Patil³

2023

ujar

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Agriculture produce especially sugarcane crop is no exception to diseases as compared to the other crops. Sugarcane diseases can be mitigated more successfully if they are identified properly and in the early stages of the diseases. Disease prone sugarcane crop directly affects the production quality and quantity. Sugarcane infections are a cause of worry for the farmers because they can wipe out the entire crop field, causing financial loss. Researchers are working on applying Artificial Intelligence (AI) techniques, like Machine Learning (ML) and Deep Learning (DL), to analyse the agricultural data (yield prediction, selling price forecasting, climate, and soil quality etc.) and prevent crop damage due to various reasons, diseases being one of them. Sugarcane producing farmers are required to be enriched with real-time data analysis using various computational techniques along with managing huge datasets. Deep neural network which includes Convolutional Neural Network (CNN) is a modern technique for agricultural disease detection. Hence, this paper presents the feasibility study and the effectiveness of DL based CNN algorithm in the disease detection of crops with special reference to selective four diseases of sugarcane crop in India. The study was prompted by the rapid evolution of sugarcane disease classes and farmers' lack of disease diagnostic and recognition skills. To solve this challenge, deep learning and computer vision are adopted. By categorizing sugarcane images into two groups: healthy and unhealthy/diseased (with disease type), the trained model is able to fulfill its goal. Using a simple CNN with four discrete classes, the analysis shows an accuracy of 98.69% for sugarcane disease detection. Further to guide the farmers, a web-based application is developed for sugarcane crop disease detection and implemented to fetch and monitor the data about the diseases. The paper also puts forth a few future research areas such as: (i) the user can enter the feedback which can be tuned with the model for accurately predicting the sugarcane crop diseases and dynamic database update; (ii) the effect of disease detection can be combined with agricultural productivity enhancement and price forecasting using AI tools and techniques, thereby aiding the farmers in effective decision making.

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“…Hence, only sugarcane fields were considered for yield modeling. The growth of sugarcane depends on a variety of factors, such as the planting season, soil type [27], fertilization, climate parameters, and irrigation. There are three planting seasons in India, i.e., adsali (July-August), pre-seasonal or autumn (October-November), and seasonal (January-March) [28].…”

Section: Agronomic Informationmentioning

confidence: 99%

Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India

Jha¹,

Patil²,

B.U³

et al. 2022

ujar

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The integration of remote sensing (RS) technology with machine learning (ML) algorithms can facilitate accurate prediction of sugarcane yield. This paper presents an assessment of the random forest (RF)-based prediction model and second-degree polynomial regression models for sugarcane yield prediction. The models are developed utilizing vegetation indices (VIs) computed from the Sentinel-2 satellite and sugarcane yield data. The sugarcane yield data were acquired from sugarcane fields around the Godavari Bio-refineries Limited (GBL) factory in Karnataka, India, during the 2017-2018 sugarcane growing season. A dataset detailing agronomic information and VIs was prepared for yield prediction. The study area comprises seven sugarcane growing talukas. The second-degree polynomial regression was used for predicting the sugarcane yield as it had the best fit for the distribution of variables. The green normalized difference vegetation index (GNDVI) recorded the highest R2, i.e., 0.71 during November month with a coefficient of variance of 0.83, with all other indices characterized by R2 values ranging from 0.42(modified chlorophyll absorption ratio index) to 0.69 (normalized difference red edge), suggesting the GNDVI's potential for sugarcane yield prediction. Comparing the actual yield with the predicted yield, the RF prediction and second-degree polynomial regression model exhibited accuracies of 90.42% and 88%, respectively. This indicates that the models are sufficiently accurate and beneficial in decision-making for sugar mill operational planning.

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A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Cited by 7 publications

References 38 publications

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Sugarcane Disease Detection Using CNN-Deep Learning Method: An Indian Perspective

Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India

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