Crop Prediction using Machine Learning

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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“…[17], [40], [49], [50], [51], [63], [64], [65], [68], [70], [72], [80], [93], [94], [99], [101], [103], [104], [105], [106], [113]…”

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Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…The use of Machine Learning (ML) and DL has also been actively researched for improved crop yields [50], agriculture advisory systems [51]- [52], detection of crop diseases, weed detection [53], and pests [38]. Zeynep et al [21] have carried out an exhaustive literature survey on the use of DL techniques in smart agriculture.…”

Section: References / Yearmentioning

confidence: 99%

IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

2022

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Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social when smart agriculture systems will be widely adopted by the farmers globally.

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“…Another model to suggest the right crop to be planted based on the soil and climatic conditions were developed by Kalimuthu et al 22 A supervised ML model Naïve Bayes Gaussian classifier was deployed, and the seed data such as the moisture, temperature, and humidity were utilized for prediction. The model was improved with an additional boosting algorithm to attain higher prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Hybrid deep‐Q Elman neural network for crop prediction and recommendation based on environmental changes

Shingade

Mudhalwadkar

2022

Concurrency and Computation

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Crop recommendation is a potential research topic that relies on environmental conditions such as temperature, humidity, rainfall, and soil pH to identify suitable crops for cultivation. There are diverse models available in the literature for crop recommendation. Still, those models are not accurate enough to predict the appropriate crop when there is a sudden change in the environmental factors. The models cannot map the raw data exactly with the prediction values, and the output relies on the quality of the input features used. To resolve these issues, the concept of Q‐learning is hybridized with deep learning to enable exact mapping of the raw data with the prediction values. In this article, Q‐learning is combined with the Elman neural network, trained with the input parameters selected by the improved Archimedes optimization algorithm from the dataset. The model evaluations are carried out with the user dataset constructed using the sensor information collected from the regions of Maharashtra. The overall accuracy provided by the proposed crop recommendation model is 99.44%, and the average inference time of the model is 0.0117 s.

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Crop Prediction using Machine Learning

Cited by 102 publications

References 1 publication

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

Hybrid deep‐Q Elman neural network for crop prediction and recommendation based on environmental changes

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