Early Detection of Pomegranate Disease Using Machine Learning and Internet of Things

Pawara, Sona; Nawale, Dnyanesh; Patil, Kunal; Mahajan, Rakesh Kumar

doi:10.1109/i2ct.2018.8529583

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…A machine learning algorithm is used for crop condition identification, early disease detection, targeted crop protection, and recommendation. It is a way to focus on how the recommender system is used in agriculture for disease detection and prediction [ 5 , 26 ].…”

Section: Related Workmentioning

confidence: 99%

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Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning

Indu

Baghel

Bhardwaj

et al. 2022

Computational Intelligence and Neuroscience

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Pesticides are chemicals used to eradicate pests. Not only are they used for plant protection and livestock in agriculture, but they are also used in public areas to kill mosquitoes, cockroaches, and other pests. Approximately 95% of the pesticides produced are only used in agriculture for crop protection. Every country wants to increase crop production. To protect their crops from pests, farmers must use pesticides. Exposure to pesticides is increasing day by day, whether occupationally or environmentally. This has resulted in an increase in crop production, but it has numerous adverse effects on human health, animal health, and the environment. Farmers repeatedly use the same pesticides on their crops, which is detrimental to human health and the environment. In this research, according to authors, the repetition of pesticides in agriculture is controlled using adjuvant and machine learning algorithms. An adjuvant is a chemical agent that is inserted within the pesticide product for enhanced pesticide performance. By utilizing an algorithm for machine learning, it is no longer necessary to repeatedly spray the same pesticide over the entire crop field in order to determine which sections of the crop field still require repeated pesticide spraying. In this research, the authors predict that 72.5% of insecticides are used in India. Logical regression classification, polynomial regression, and K-nearest neighbor algorithm (KNN) are applied to detect this required field.

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Section: Related Workmentioning

confidence: 99%

“…Crops and plants are sprayed with pesticides for various reasons. Pesticides are used to protect crops and plants from insecticides [ 3 – 5 ]. They are also used for the growth of crops and plants.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning

Indu

Baghel

Bhardwaj

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…[7] tries to diagnose diseases in pomegranates and fin this research work feature extraction is done using GLCM method.The ANN classification using Back Propagation algorithm has resulted in an overall accuracy of 90%.Prof. Sona Pawara, Dnyanesh Nawale [8], Kunal Patil, Rakesh Mahajan says early detection of pomegranate disease using machine learning and Internet of Things can be done and also suggested a working model for the same.…”

Section: Literature Reviewmentioning

confidence: 99%

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

Vasumathi¹,

Kamarasan²

2021

INDJCSE

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Pomegranate is a widely grown plant in India. This highly beneficial fruit is infected by multiple pests and diseases which cause great economical losses. Different forms of pathogen diseases on leaf, stem and the fruits are present. Some of the diseases that affect pomegranate fruits are anthracnose, cercospora, heart rot and bacterial blight. There is a need for disease control strategies to incorporate timely action on the developed diseases. Thus, there is a need for intelligent and self-learning recognition systems to detect these diseases on time. This study is aiming to classify pomegranate fruits into two classes normal and abnormal using CNN LSTM technique. This research work uses a hybrid CNN-LSTM technique to detect four types of diseases present in the pomegranate fruits and classify them into four classes. The results obtained using CNN LSTM are then optimized using dragonfly algorithm. The features like colour, texture and shape of the fruits are collected and fed into the hybrid CNN-LSTM. The dataset for the classifier is given as an excel file which is initially pre-processed using map reduce technique and dimensionality reduction carried using Principal Component Analysis and Discriminant analysis. The CNN LSTM classifier identifies the 4 types of diseases and normal fruit. The classification is further optimized using dragonfly algorithm. The optimized weight and cost function has further explored to support the multi-class disease detection process. Experimental results have shown an accuracy of 92% in classification using CNN-LSTM technique and optimization using dragonfly techniques shows an improved classification accuracy of 97.1%.

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“…The designed system is capable of displaying the analytical results and the position of the plant where the disease is present. Sona Pawara et al [154], studied the pomegranate diseases such as Bacterial Blight, Fruit Spot, Fruit Rot, and Leaf Spot. Developed a HMM and senor based model to early detect the disease and provide the solutions.…”

Section: Iot In Disease Detectionmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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Early Detection of Pomegranate Disease Using Machine Learning and Internet of Things

Cited by 15 publications

References 7 publications

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning

An LSTM Based CNN Model for Pomegranate Fruit Classification With Weight Optimization Using Dragonfly Technique

Recent Developments of the Internet of Things in Agriculture: A Survey

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