Analysis of crop suitability using clustering technique in Coimbatore region of Tamil Nadu

S, Poongodi; Babu, M. Rajesh

doi:10.1002/cpe.5294

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contribution Dataset [51] A new datamining technique was proposed to cluster crops based on their suitability compared to soil nature of different areas.…”

Section: Reference Yearmentioning

confidence: 99%

“…If the comparison falls in a prede ned range, they generate a rank for the combination of crops and lands. In another study [51], authors developed a new data mining technique to cluster the crop based on the suitability of a crop against the soil nature of areas. Features are extracted from the datasets using ve different feature extraction metrics, such as, pH distance calculation, NPK (Macro nutrition distance calculation), MICRONUT (Micro nutrition distance calculation), water requirements, and temperature requirements.…”

Section: How Did Research About Cr Evolved Over Time?mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

View full text Add to dashboard Cite

Crop selection (CS) is one of the most critical elements that affects the final yield directly. As a result, selecting an appropriate crop is always a critical decision that a farmer must make, considering environmental factors. Choosing an appropriate crop for a given farm is a difficult decision including a plethora of variables that influence the final yield. Experts are frequently consulted to assist farmers with CS; but, as this alternative is time consuming and expensive, it is not available to many farms. The use of recommender systems (RSs) in agricultural management has recently brought some captivating and promising results. We propose a systematic literature review (SLR) in this article to find and provide the most relevant and high-quality publications ad- dressing the crop recommendation (CR) question. The core concept of this SLR is inspired from the guidelines of PRISMA 2020.The different CR approaches are discussed, as well as all the most important input features for recommendation, which are determined and classified. We also identified some of the biggest hurdles to using CR in agriculture. Besides, we made an inventory of the most used techniques for CR. Further, we made an inventory of evaluation criteria and evaluation approaches.

show abstract

“…Contribution Dataset [51] A new datamining technique was proposed to cluster crops based on their suitability compared to soil nature of different areas.…”

Section: Reference Yearmentioning

confidence: 99%

Section: How Did Research About Cr Evolved Over Time?mentioning

confidence: 99%

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Plant growth and development are affected by a great number of factors such as extreme temperature, radiation, salinity, drought, and so on [1]. Both UV-B radiation and low temperature (LT) have adverse and profound influences on plants [2].…”

Section: Introductionmentioning

confidence: 99%

The Physiological Mechanism of Low-Temperature Tolerance Following the UV-B Radiation of Eucommia ulmoides Oliver

Zhang,

Tian,

Zhou

et al. 2024

Agriculture

View full text Add to dashboard Cite

Eucommia ulmoides Oliver with rich active components, such as flavonoids, lignans, polysaccharides, is used as a medicinal plant. Unfortunately, its popularization and cultivation are limited due to its low-temperature sensitivity. In this study, we aimed to explore the effect of different doses of ultraviolet-B (UV-B) radiation (UV-1, UV-2, and UV-3) and low-temperature (LT) stress, both applied individually and in combination, on the photosynthetic properties, biochemical parameters, and the contents of salicylic acid in E. ulmoides plants. The results showed that UV-B radiation alone significantly reduced photosynthetic performance and soluble total sugar content, as well as causing increases in soluble protein, proline, and superoxide anion content and antioxidant activity including SOD, POD, CAT, total phenol, and total flavonoid content. The leaf thickness and photosynthetic parameters significantly increased, as well as a significant decrease in SOD activity and soluble sugar, proline, and superoxide anion content after 14 days of none-UV-B radiation exposure. UV-B combined with LT significantly improved photosynthetic properties, Chl content, and soluble sugar content but significantly decreased proline content. Principal component analysis showed that salicylic acid was the key factor in improving LT tolerance, and UV-2 radiation showed the best LT resistance. We aim to provide new ideas and a theoretical basis for the directional cultivation and LT stress tolerance research of E. ulmoides. Our findings demonstrate that the combined effect was more positively helpful in improving the ability to resist LT tolerance via the improvement of photosynthetic ability and the increase in soluble sugar and salicylic acid content in E. ulmoides.

show abstract

“…In smart agriculture, machine learning is mainly used to predict the yield of crops or identify crop diseases [ 16 , 17 ]. Still, there are still few applications in crop suitability classification [ 18 , 19 ], especially in the ecological suitability of blueberries. Therefore, this paper proposed a CatBoost-based classification method for the ecological suitability of blueberries.…”

Section: Introductionmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.

show abstract

Analysis of crop suitability using clustering technique in Coimbatore region of Tamil Nadu

Cited by 5 publications

References 8 publications

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

The Physiological Mechanism of Low-Temperature Tolerance Following the UV-B Radiation of Eucommia ulmoides Oliver

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Contact Info

Product

Resources

About