Areca Nut Disease Detection Using Image Processing

Rajendra, A. B.; Rajkumar, N.; Shetty, Pallavi

doi:10.1007/978-981-15-4032-5_83

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…The image captured by the camera is transmitted to the Raspberry PI 4 MODULE. Figure 4 shows the developed algorithm of operation of the smart system [10], [15], [16]. The RASPBERRY PI 4 MODULE is a single-board computer with a small size.…”

Section: Methodsmentioning

confidence: 99%

Smart system for early detection of agricultural plant diseases in the vegetation period

Baratov,

Valixanova

2023

E3S Web Conf.

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This paper presents a smart system for the early detection of agricultural plant diseases in the vegetation period. The proposed smart system allows the detection of three types of wheat diseases, particularly yellow rust, powdery mildew, and Septoria at an early stage, and significantly improves the soil and ecology by locally spraying harmful chemicals just on sick plants. The proposed disease-detecting method is based on the structure of a convolutional neural network (CNN) using the Pycharm program based on the C ++ programming language. The basic structure of the smart system consists of Raspberry PI 4 MODULE, Raspberry Pi camera module v2, buzzer, HC-SR04 distance sensor, rotor driver, AC motor, power supply, relay microelectronics, and some digital devices.

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Section: Methodsmentioning

confidence: 99%

Smart system for early detection of agricultural plant diseases in the vegetation period

Baratov,

Valixanova

2023

E3S Web Conf.

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“…For agricultural disease diagnosis, a solution with an end-to-end Android application built with TFLite and CNN algorithms was presented by Rajendra et al ( 2020 ). In order to efficiently support and aid greenhouse farmers, a DL detection method was employed to identify crop illnesses by photographing the leaves and comparing them to data sets (Rajendra et al ( 2020 )). Furthermore, users are directed to an e-commerce website where different pesticides, as well as their prices and usage directions, are provided.…”

Section: Review Of Related Workmentioning

confidence: 99%

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi,

Ezugwu,

Akinyelu

et al. 2024

Environ Monit Assess

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Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

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“…Still, very little research is being done on weather-based areca nut disease prediction. Recently authors [20], [34] detected areca nut disease using a convolution neural network model and classified it as a healthy and diseased crop based on images. In recent years many journals and conference articles have been published on the different optimisation algorithms and deep learning techniques used to solve real-world problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing and Optimizing RNN Models to Predict Fruit Rot Disease Incidence in Areca Nut Crop Based on Weather Parameters

Krishna,

Prema

2023

IEEE Access

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A farmer faces several challenges associated with fruit rot disease in the areca nut crop. Weather factors, including rainfall and temperature, largely influence the disease severity and spread of infection in crops. Significant growth has been achieved using RNN models for time series forecasting in the past few decades, but these models are not applied to fruit rot disease prediction. This study introduces Vanilla GRU, Stacked GRU, Bidirectional GRU, and Bidirectional LSTM models for the inaugural prediction of fruit rot disease scores using past weather data. The current investigation also involves the mitigation and comparison of forecast inaccuracies by utilising weight optimisation algorithms like Adam, Adagrad, RMSprop, and Genetic algorithms. Meteorological and disease score data are gathered from the Agricultural Research Station in Brahmavar, Karnataka, and CPCRI Kasargod, Kerala. These datasets are combined using a rule-based algorithm to train and evaluate the proposed model. Empirical outcomes reveal that the vanilla GRU model, when fine-tuned with the Adam algorithm, exhibits a diminished Mean Squared Error (MSE) value of 0.0009, an exceptionally minimal Mean Absolute Error (MAE) value of 0.02, and an elevated R-squared (R2) score of 0.99. Similarly, the Bidirectional LSTM model, optimised through RMSprop, yields an impressively low Root Mean Squared Error (RMSE) value of 0.033. Generally, the optimised Deep Learning (DL) models consistently demonstrate enhanced predictive precision compared to alternative models. In conclusion, the anticipation of areca nut disease, as facilitated by this study, stands to aid farmers in curtailing unnecessary fungicide application and achieving more favourable yields.

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Areca Nut Disease Detection Using Image Processing

Cited by 14 publications

References 4 publications

Smart system for early detection of agricultural plant diseases in the vegetation period

Smart system for early detection of agricultural plant diseases in the vegetation period

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Constructing and Optimizing RNN Models to Predict Fruit Rot Disease Incidence in Areca Nut Crop Based on Weather Parameters

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