Detecting the Infectious Area Along with Disease Using Deep Learning in Tomato Plant Leaves

Juyal, Piyush; Sharma, Sachin

doi:10.1109/iciss49785.2020.9316108

Cited by 17 publications

(2 citation statements)

References 3 publications

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“…Verma et al [27] used MobileNetV2 with lightweight depthwise convolutions to improve computation time and accuracy. Juyal et al [28] adopted the R-CNN mask for correct identification and accurately mask the disease-affected region for fast identification. This work aims to utilize the power of CNN in a rice plant disease detection task.…”

Section: Related Workmentioning

confidence: 99%

Generic Paddy Plant Disease Detector (GP2D2)

Prathima¹,

G²,

Sivachandar³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too.

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Section: Related Workmentioning

confidence: 99%

Generic Paddy Plant Disease Detector (GP2D2)

Prathima¹,

G²,

Sivachandar³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…Additionally, Piyush et al proposed an R-CNN algorithm based on deep learning to correctly identify infected areas of tomato leaf diseases in India. In this manner, they helped farmers diagnose diseases and intervene in time to improve the survival rate of crops [14]. Choi et al proposed a CNN based on Inception-ResNet v2 to identify mineral nutrients from growth images of tomato plants in a greenhouse.…”

Section: Introductionmentioning

confidence: 99%

YOLOv4-Driven Appearance Grading Filing Mechanism: Toward a High-Accuracy Tomato Grading Model through a Deep-Learning Framework

et al. 2022

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In traditional agricultural quality control, agricultural products are screened manually and then packaged and transported. However, long-term fruit storage is challenging in tropical climates, especially in the case of cherry tomatoes. Cherry tomatoes that appear rotten must be immediately discarded while grading; otherwise, other neighboring cherry tomatoes could rot. An insufficient agricultural workforce is one of the reasons for an increasing number of rotten tomatoes. The development of smart-technology agriculture has become a primary trend. This study proposed a You Only Look Once version 4 (YOLOv4)-driven appearance grading filing mechanism to grade cherry tomatoes. Images of different cherry-tomato appearance grades and different light sources were used as training sets, and the cherry tomatoes were divided into four categories according to appearance (perfect (pedicled head), good (not pedicled head), defective, and discardable). The AI server ran the YOLOv4 deep-learning framework for deep image learning training. Each dataset group was calculated by considering 100 of the four categories as the difference, and the total numbers of images were 400, 800, 1200, 1600, and 2000. Each dataset group was split into an 80% training set, 10% verification set, and 10% test set to overcome the identification complexity of different appearances and light source intensities. The experimental results revealed that models using 400–2000 images were approximately 99.9% accurate. Thus, we propose a new mechanism for rapidly grading agricultural products.

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A Comprehensive Review on Automatic Detection and Early Prediction of Tomato Diseases and Pests Control Based on Leaf/Fruit Images

Sharma

Menghani

et al. 2023

Lecture Notes in Networks and Systems

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Detecting the Infectious Area Along with Disease Using Deep Learning in Tomato Plant Leaves

Cited by 17 publications

References 3 publications

Generic Paddy Plant Disease Detector (GP2D2)

Generic Paddy Plant Disease Detector (GP2D2)

YOLOv4-Driven Appearance Grading Filing Mechanism: Toward a High-Accuracy Tomato Grading Model through a Deep-Learning Framework

A Comprehensive Review on Automatic Detection and Early Prediction of Tomato Diseases and Pests Control Based on Leaf/Fruit Images

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