Precision Agriculture for Banana Using Wireless Sensor Network

Pujari, Shankar; Bogiri, Nagaraju

doi:10.1109/iccubea.2017.8464007

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…If the count reaches 36 within two days, it indicates that the farm has experienced two days of favorable weather conditions for Sigatoka disease, such as high or extremely high relative humidity, rainfall, and suitable temperatures. Thanks to the algorithm's continuous and accurate monitoring of the WSN (Wireless Sensor Network), crop losses caused by disease attacks are prevented, and farmers receive timely warnings [8].…”

Section: Algorithmmentioning

confidence: 99%

A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine

Perla,

Choda,

Yelchuru

et al. 2024

RIA

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Agriculture is a strong foundation of economy for many agriculture-based countries like India. In the economies of many nations, including India, where banana farming is quite important, agriculture is a key component. Musa Acuminata is the scientific name for the banana, and a banana tree normally lives for about 25 years. Banana cultivation typically lasts 9 to 12 months. The persistent threat of illnesses, which can drastically affect yield and quality, is just one of the many difficulties faced by the banana industry. This study focuses on overcoming these obstacles by observing the health of the soil and detecting infections in banana plants. The yield and crop quality of bananas can be negatively impacted by a number of variables, including illnesses. Developing an efficient method for spotting diseases in banana plants is our main goal, and we're also constantly keeping an eye on the condition of the soil. The main methodology we use for identifying diseases is SVM. In addition, we use procedures for soil monitoring to measure soil pH levels, minerals, and other essential elements that affect the health of the soil. The study shows good results in identifying diseases, ensuring prompt treatments to reduce risks associated with diseases. Furthermore, strong plant growth and increased yields are facilitated by realtime soil health monitoring. By tackling the ongoing threat of illnesses in banana cultivation through SVM-based disease identification and ongoing soil health monitoring, this research offers a vital contribution to the agricultural sector. The study shows promise in identifying diseases and guaranteeing prompt actions to reduce risks associated with diseases. Furthermore, strong plant growth and increased yields are facilitated by real-time soil health monitoring. This paper is focused on both types of diseases of banana plant and minerals in the soil.

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Section: Algorithmmentioning

confidence: 99%

A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine

Perla,

Choda,

Yelchuru

et al. 2024

RIA

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“…In [73], with Wavelet, Tamura, and Gabor transforms, the application of a backscattering image acquisition with several methodologies of transformdependent image texture analysis to evaluate the banana quality at various ripening stages was examined. In [74], the context-based optimal pattern was proposed and recommended to farmers for the following season in order to produce high-quality bananas with the most efficient resources. As per the model, the user will be notified of the disease and pest attack caused by the Sigatoka disease with an accuracy of 74%.…”

Section: Figure 10 Division As Per the Area Of Research On The Bananamentioning

confidence: 99%

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

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for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

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“…Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture [1], [2], [3], [4], [5]. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment

Alex¹,

Kanavalli²

2019

IJITEE

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Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture. There are several challenges of crops yield prediction in the field of precision agriculture are (i). Obtain minimized production due to climate change; (ii). Lead to different diseases; (iii). Availability of Water; (iv). No awareness of fertilizers and crop features; (v). Climate change; (vi). Unexpected weather events.Other loss factors in the agriculture are lowly seed quality, unplanned irrigation and exploitation of insecticides and fertilizers. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model. Hence in this research our focus is on optimizing the significant parameters such as rainfall, temperature and fertilizers rate to obtain the P-values for testing the crop and also analyze the human health safety (farmers and suppliers) due to the dynamic change of environment and also soil nutrients. Big data analytics is the feasible platform to test and measure the crop grow in the particular agriculture field. It helps in climate, weather events prediction and also it is used to compute the sufficient resources for crop cultivation.

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Precision Agriculture for Banana Using Wireless Sensor Network

Cited by 6 publications

References 8 publications

A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine

A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment

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