scholarly journals Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021 ◽  
Vol 8 ◽  
Author(s):  
Sufola Das Chagas Silva Araujo ◽  
V. S. Malemath ◽  
K. Meenakshi Sundaram

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

Author(s):  
Vivek K. Verma ◽  
Tarun Jain

The disease occurrence phenomena in plants are season-based which is dependent on the presence of the pathogen, crops, environmental conditions, and varieties grown. Some plant varieties are particularly subject to outbreaks of diseases; on the other hand, some are opposite to them. Huge numbers of diseases are seen on the plant leaves and stems. Diseases management is a challenging task. Generally, diseases are seen on the leaves or stems of the plant. Image processing is the best way for the detection of plant leaf diseases. Different kinds of diseases occur because of the attack of bacteria, fungi, and viruses. The monitoring of leaf area is an important tool in studying physiological capabilities associated with plant boom. Plant disorder is usually an unusual growth or dysfunction of a plant. Sometimes diseases damage the leaves of plants.


To improve the accuracy of plant leaf image recognition with a small dataset of plant leaves, a convolution neural network (CNN) plant leaf image recognition method based on transfer learning is proposed. First, a plant leaf image database was expanded by pre-processing the original plant leaf images through random horizontal and vertical rotation and random zooming. The expanded dataset was then processed by mean removal and divided into training and testing sets at a ratio of 4:1. Second, transfer learning training was performed on the plant leaf dataset using existing models (AlexNet and InceptionV3) that were pre-trained on a large dataset. To ensure these models can be adapted to image recognition for plant leaves, the original parameters of the last fully connected layer were replaced, whereas those of all other convolution layers were retained. Finally, the method proposed in this paper was compared to support vector machine, deep belief network, and CNN through testing on the ICL database. A Tensorflow training network model was used in the comparison test, and the results were visualized by Tensorboard. The testing results showed a considerable improvement in recognition accuracy when using the pre-trained AlexNet and InceptionV3 models, where the training dataset accuracies were 95.31% and 95.4%, respectively.


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 511
Author(s):  
Syed Mohammad Minhaz Hossain ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.


Author(s):  
Swati Singh ◽  
Sheifali Gupta ◽  
Ankush Tanta ◽  
Rupesh Gupta

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20818-20827 ◽  
Author(s):  
Zhi Zhang ◽  
Ruoqiao Jiang ◽  
Shaohui Mei ◽  
Shun Zhang ◽  
Yifan Zhang

2010 ◽  
Vol 18 (3) ◽  
pp. 188-195 ◽  
Author(s):  
Algimantas Sirvydas ◽  
Vidmantas Kučinskas ◽  
Paulius Kerpauskas ◽  
Jūratė Nadzeikienė ◽  
Albinas Kusta

Solar radiation energy is used by vegetation, which predetermines the existence of biosphere. The plant uses 1–2% of the absorbed radiant energy for photosynthesis. All the remaining share of the absorbed energy, accounting for 99–98%, converts into thermal energy in the plant leaf. At the lowest wind under natural surrounding air conditions, plant leaves change their position with respect to the Sun. An oscillating plant leaf receives a variable amount of solar radiation energy, which causes changes in the balance of plant leaf energies and a changing emission of heat in the leaf. The analysis of solar radiation energy pulsations in the plant leaf shows that when the leaf is in the edge positions of angles 10°, 20° and 30° with respect to the Sun, 1.5%; 6% and 13% less of radiation energy reach the leaf, respectively. During periodic motion, when the amplitude of leaf oscillation is no bigger than 10°, the plant surface receives up to 1.6% less of solar radiation energy within a certain period of time, and when the amplitude of oscillation reaches 30° up to 14% less of solar radiation energy reach the leaf surface. The total amount of radiant energy received during pulsations of solar radiation energy is not dependent on the frequency of oscillation in the same interval of time. Temperature pulsations occur in the leaf due to solar radiation energy pulsations when the plant leaf naturally changes its position with respect to the Sun. Santrauka Saules spinduliuotes energija būtina augalijai, kuri lemia biosferos egzistavima. Augalas 1–2 % absorbuotos spinduliuotes energijos sunaudoja fotosintezei, o 99–98 % absorbuotos energijos augalo lape virsta šilumine energija. Natūraliomis aplinkos salygomis esant mažiausiam vejui augalo lapu padetis Saules atžvilgiu keičiasi. Taigi augalo svyruojančio lapo gaunamas Saules spinduliuotes energijos kiekis yra kintamas, tai sukelia pokyčius augalo lapo energiju balanse ir kintama šilumos išsiskyrima lape. Analizuojant Saules spinduliuotes energijos pulsacijas augalo lape, nustatyta, kad, lapui esant kraštinese 10°, 20° ir 30° kampu padetyse Saules atžvilgiu, i ji atitinkamai patenka 1,5 %; 6 % ir 13 % mažiau spinduliuotes energijos. Augalo lapui periodiškai svyruojant, kai svyravimo amplitude yra iki 10°, per tam tikra laika i lapo paviršiu patenka iki 1,6 % mažiau Saules spinduliuotes energijos, o kai svyravimo amplitu‐de siekia iki 30°, – iki 14 % mažiau. Saules spinduliuotes energijos pulsaciju metu gautas bendras spinduliuotes energijos kiekis nepriklauso nuo to paties laiko intervalo svyravimo dažnio. Del Saules spinduliuotes energijos pulsaciju, natūraliai keičiantis augalo lapo padečiai Saules atžvilgiu, lape kyla temperatūros pulsacijos. Резюме Растения потребляют солнечную лучевую энергию, которая является основой существования биосферы. 1–2% абсорбированной лучевой энергии они используют на фотосинтез. В натуральных условиях при малейшем дуновении ветра листья растений меняют свое положение относительно Солнца. Колеблющийся лист получает переменное количество лучевой энергии, которое вызывает изменения в энергетическом балансе листа растения, что сказывается на переменном выделении тепла в листе. Анализируя пульсации солнечной лучевой энергии в листе растения, установлено, что при крайних положениях листа относительно Солнца на 10, 20 и 30 градусов на лист попадает соответственно на 1,5%, 6% и 13% меньше лучевой энергии. При периодическом колебании листа, когда амплитуда его колебания составляет 10 градусов, за известный промежуток времени солнечная лучевая энергия, попадающая на поверхность листа, уменьшается до 1,6%, а при амплитуде колебания до 30 градусов соответственно количество лучевой энергии на поверхности листа растения уменьшается до 14%. Установлено, что суммарное количество солнечной лучевой энергии во время пульсации не зависит от частоты колебания листа за одинаковый промежуток времени. Пульсации солнечной лучевой энергии при изменении положения листа растения относительно Солнца вызывают температурные пульсации в листе.


Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe

Machine learning systems use different algorithms to detect the diseases affecting the plant leaves. Nevertheless, selecting a suitable machine learning framework differs from study to study, depending on the features and complexity of the software packages. This paper introduces a taxonomic inspection of the literature in deep learning frameworks for the detection of plant leaf diseases. The objective of this study is to identify the dominating software frameworks in the literature for modelling machine learning plant leaf disease detecting systems.


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