Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming

Thorat, Tanmay; Patle, B.K.; Kashyap, Sunil Kumar

doi:10.1016/j.atech.2022.100114

Cited by 30 publications

(11 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance [ 44 ], SVM and DT models demonstrated accuracy rates of 91.73 % and 85.07 %, respectively. Another study [ 51 ], focused on smart farming and incorporated an intelligent insecticide and fertilizer recommendation system. This study achieved an accuracy of 78 % for SVM and 75 % for KNN models.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Dey,

Ferdous,

Ahmed

2024

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Dey,

Ferdous,

Ahmed

2024

Heliyon

View full text Add to dashboard Cite

“…The pesticide recommendation operation is effective and compact because to the TPF-CNN dual operator technique. When compared to other methods like KNN, SVM, and ANN, TPF-CNN performs about 20% better [44].…”

Section: Fertilizers Recommendationmentioning

confidence: 93%

“…The main focus of livestock management is the raising of cattle, including sheep, pigs, and other animals, for human consumption of their flesh. ML techniques can Tanmay Thorat et al [44] increase livestock production efficiency by using livestock management techniques for feeding the animal. Precision livestock farming is used by using the sensors to monitor the multiple animals at the same time with accuracy [3].…”

Section: Livestock Managementmentioning

confidence: 99%

“…However, excessive use of insecticides and fertilizers in farming puts human health at risk; as a result, it is important to control these practices in order to ensure healthy crop production. A soil NPK sensor is used to do soil nutrient analysis, and fertilizer recommendations are made in accordance with the results of the analysis [44].…”

Section: Fertilizers Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Review of Machine Learning Model Applications in Precision Agriculture

Baburao¹,

Kulkarni²,

Kharade³

et al. 2023

Advances in Computer Science Research

View full text Add to dashboard Cite

Over the past two decades, modern agriculture has made significant advancements. The methods used in farming have changed from conventional ways to digital technologies as a result of significant technology improvement. Advances in machine learning and artificial intelligence are being applied in this discipline to reevaluate farming practices in order to meet the demands of an expanding population. Throughout the entire cycle of planting, growing, and harvesting, machine learning is prevalent. It starts with the planting of a seed in the ground, goes through soil preparation, seed breeding, crop health monitoring, measuring water feed and concludes with the harvest being picked up by robots by using computer vision techniques.For crop selection, yield prediction, soil classification, weather forecasting, irrigation system, fertilizer prescription, disease prediction, and determining the minimal support price, machine learning models are developed in the field of precision agriculture. In this article we will cover the different categories of precision agriculture applications and use of machine learning models in those different categories. Various models in precision agriculture include Artificial Neural Networks, Support Vector Machines (SVMs), Convolution Neural Networks (CNN), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means Clustering. The ultimate solution to issues in agriculture rests in the efficient application of Machine Learning (ML). ML can bring about a paradigm change in nations like India where agriculture is the main source of employment. Since most Indian rural areas have adopted digitalization, ML and AI-related applications are gradually emerging in this sector.

show abstract

“…Machine learning models such as SVM, RF, and Linear regression are used for creating the system which takes the input soil water parameters, previous water usage in the field and meteorological information. The models thus developed for irrigation decision support system predicts the future usage with lower prediction error [8] [20].…”

Section: Algorithm Employedmentioning

confidence: 99%

Precision Agriculture Model for Farm Irrigation using Machine Learning to Optimize Water Usage

Patil,

Kulkarni,

Patil

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.

show abstract

Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming

Cited by 30 publications

References 26 publications

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables

Review of Machine Learning Model Applications in Precision Agriculture

Precision Agriculture Model for Farm Irrigation using Machine Learning to Optimize Water Usage

Contact Info

Product

Resources

About