A Review on Leaf-Based Plant Disease Detection Systems Using Machine Learning

Tomar, Abhishek; Gupta, Gaurav; Salehi, Ahmad Waleed; Vanipriya, Ch.; Kumar, Nagesh; Sharma, Bhanu

doi:10.1007/978-981-16-8248-3_24

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Additionally, it offers soil cultivation a greener alternative. Food production depends on the economy and also on the yield of agricultural production [21].…”

Section: Volume 4 2016mentioning

confidence: 99%

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Sharma,

Dadheech,

Aneja

et al. 2023

IEEE Access

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Agriculture contributes a significant amount to the economy of India due to the dependence on human beings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper's primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decisionsupport tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop's growing season. It can decide which crops to produce and what to do in the crop's growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques -convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model's errors compare to those of the other methods.

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“…Additionally, it offers soil cultivation a greener alternative. Food production depends on the economy and also on the yield of agricultural production [21].…”

Section: Volume 4 2016mentioning

confidence: 99%

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Sharma,

Dadheech,

Aneja

et al. 2023

IEEE Access

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“…In this way, it is important to move toward fostering a quick and exact groundnut leaf sickness acknowledgment technique to reasonably increment efficiency [12]. The development of most significant field yields like rice, wheat, maize, soybean, and sugarcane are affected because of disease of various bugs and the gather creations are diminished because of various kinds of insects [14]. The characterization and ID of a wide range of yield bugs accurately is a troublesome errand for the ranchers because of the comparative appearance in the previous phase of yield development.…”

Section: Literature Surveymentioning

confidence: 99%

Pest Detection and Classification in Peanut Crops Using CNN, MFO, and EViTA Algorithms

Venkatasaichandrakanthand,

Iyapparaja

2023

IEEE Access

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The growth of vision transformer (ViT) methods have been quite enormous since its features provide efficient outcome in image classification, and identification. Inspired of this beneficial, this paper propose an Enhanced vision transformer architecture (EViTA) model for pest identification, segmentation, and classification. The as of late found that, compare to machine learning, Convolutional neural network algorithms the ViT has providing trusted results on image classification. Motivated by this, in this paper, we concentrate on the best way to learn dual barch segment representations in ViT models for image arrangement. Based upon its features, here propose a double layer transformer encoder to integrate pest image segments of various sizes of pest images to create more grounded image highlights. The current study uses, three pest datasets that affects peanut crops such as Aphids (IP102 Dataset), Wireworm (IP102 Dataset), and Gram Caterpillar collected from public available repository. Our methodology processes small segment and huge segment of tokens with two separate parts of various computational intricacy also, these tokens are then combined simply by consideration numerous times to complete one another. The taken datasets' are preprocessed utilizing the characteristic by using moth flame optimization (MFO), and flatten the images by using linear projector methodology to enhance the missing quality in pest images, and afterward normalization methods are executed to switch it over completely in to mathematical arrangement. This processed information is standardized further utilizing the self attention in StandardScaler procedures are carried out for choosing the ideal highlights in the dataset accordingly having huge effect towards affecting pest image predictions. These ideal highlights are at last taken care of into the EViTA model and the outcomes created are considered in contrast to the cutting edge models which at last legitimize the predominance of the proposed EViTA+PCA+MFO model in pest image prediction with high accuracy rate. Broad trials show that our methodology performs better compared to or on standard with a few simultaneous deals with vision transformer, notwithstanding productive CNN models.INDEX TERMS Pest, peanut, moth flame optimization, CNN, vision transformer.

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“…Researchers have also developed more accurate classification models by studying the recent research done by different researchers to diagnose plant diseases, which can be advanced image processing systems [12]. In this regard, the classification model can be used in a study to diagnose and classify diseases (mosaic, leaf rust, and round spot) of apple tree leaves as well as diseases (brown spot, grey spot, and round spot) of corn.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application

Gan,

Zhou,

Cui

et al. 2023

IJACSA

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Plant diseases significantly harm agriculture, which has an impact on nations' economies and levels of food security. Early plant disease detection is essential in smart agriculture. For the diagnosis of plant diseases, a number of methods, including imaging, have been used recently. Some of the existing methods for plant disease detection using imaging have limitations as firstly, high computational cost, some methods require complex image processing algorithms or manual design of features that can increase the time and resources needed for the detection. Secondly, low accuracy, most of the methods rely on simple classifiers or handcrafted features that may not capture the subtle differences between different diseases or healthy leaves. Thirdly, dependency on expert knowledge, some methods need human intervention or prior knowledge of the diseases and pests to perform the detection. These limitations are not suitable for the problem at hand because they can affect the efficiency of the detection system. In this study, three apple tree leaf diseases-apple black spot, Alternaria, and Minoz blightare detected using a neural network (NN) and a digital image processing technique. The sample images are prepared, processed, and used to extract attributes using a digital image processing approach, and the NN is used to classify the diseases. An evaluation of the proposed system's performance in identifying illnesses in apple trees shows satisfactory accuracy and strong overall performance. Additionally, when compared to other techniques already in use, this strategy is more effective at diagnosing.

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A Review on Leaf-Based Plant Disease Detection Systems Using Machine Learning

Cited by 3 publications

References 19 publications

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Pest Detection and Classification in Peanut Crops Using CNN, MFO, and EViTA Algorithms

A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application

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