Automation and integration of growth monitoring in plants (with disease prediction) and crop prediction

H, Kishan Das Menon; Mishra, Dipali; Deepa, D.

doi:10.1016/j.matpr.2021.01.973

Cited by 15 publications

(2 citation statements)

References 7 publications

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“…Study (Mishra 2021) examines how the Internet of Things (IoT) may be used to efficiently gather data on environmental factors such as temperature, pH, and precipitation using a variety of machine learning methods. Decision Tree to analyze agricultural data and provide recommendations about what may and cannot be planted.…”

Section: Related Workmentioning

confidence: 99%

Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach

Shree,

Gupta,

Srikanth

2023

Multidiscip. Sci. J.

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The foundation of the global and Indian economies is agriculture. Since agriculture started millions of years ago, many environments, civilizations, and technical developments have fostered and defined the evolution of agricultural technology. In this study, we examine how we may analyze images of plants and soil to better keep tabs on their health, as well as how we can determine how much water each kind of plant needs. Images of the plants and soil are first taken using a digital camera with the necessary resolution. The form and geometric characteristics are extracted from the plant images using the inner distance shape context-based descriptor and geometrical descriptors. The soil images are also used to extract features and color properties. The botanical plant species dictionary is used to identify the plant type using the contour elements of the plant photos. Gradient structured random forest (GS-RF) classification is used to forecast leaf diseases. Principal Component Analysis (PCA) and Hierarchical Gradient Deep Neural Network (HG-DNN) classification techniques are used to determine the causes of a given plant disease based on the characteristics of soil images and plant disease images. The findings are communicated to the growers through text messages sent to their mobile phones on a daily and seasonal basis, along with any potential recommendations for preventative actions.

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Section: Related Workmentioning

confidence: 99%

Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach

Shree,

Gupta,

Srikanth

2023

Multidiscip. Sci. J.

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“…Nalini et al utilized the KNN (K-Nearest Neighbor) classifier and the ensemble method to predict crop diseases based on measured temperature data [14]. Mishra et al designed a system equipped with sensors for the real-time measurement of temperature and humidity, employing a decision tree algorithm to predict the suitability of the environment for crop cultivation [15].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation

Lee,

Park,

Yang

et al. 2024

Applied Sciences

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With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method.

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Prediction of crop disease using Rank Regressive Learning and Proaftn Fuzzy Classification models

Saritha

Thangaraja²

2023

Soft Comput

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Automation and integration of growth monitoring in plants (with disease prediction) and crop prediction

Cited by 15 publications

References 7 publications

Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach

Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach

A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation

Prediction of crop disease using Rank Regressive Learning and Proaftn Fuzzy Classification models

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