Soft-Computing-Based Approaches for Plant Leaf Disease Detection

2019 ◽  
pp. 100-113
Author(s):  
Vivek K. Verma ◽  
Tarun Jain
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Soft Computing ◽  
Environmental Conditions ◽  
The Other ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Disease Occurrence ◽  
Plant Image

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.

An Improved Leaf Area Measurement Based on Image Processing

2012 ◽  
Vol 182-183 ◽  
pp. 624-628
Author(s):  
Dian Yuan Han
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Good Accuracy ◽  
Area Measurement ◽  
Scanning Method ◽  
Plant Leaf ◽  
Unknown Area ◽  
Leaf Image ◽  
Background Area

This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with 2-side scanning method. Then the leaf region was segmented according to 2G-R-B of every pixel with two different thresholds, and by using of dilatation operation, the trimap of leaf image was got. Next the pixels in unknown area were classified to the foreground or background area with improved knockout method and the exact leaf was segmented. Lastly, the leaf area was calculated according to the pixels proportion between leaf region and the referenced rectangle. Experiment results show this method has good accuracy and rapid speed.

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
Keyword(s):  
Feature Learning ◽  
Region Of Interest ◽  
Physical Method ◽  
Image Database ◽  
Features Extraction ◽  
Plant Leaves ◽  
Rotation Invariant ◽  
Plant Leaf ◽  
Plant Image ◽  
Leaf Image

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.

scholarly journals Image processing algorithm to estimate ice-plant leaf area from RGB images under different light conditions

2021 ◽  
Vol 924 (1) ◽  
pp. 012013
Author(s):  
S Islam ◽  
M N Reza ◽  
M Chowdhury ◽  
M N Islam ◽  
M Ali ◽  
...  
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Light Condition ◽  
Processing Algorithm ◽  
Artificial Light ◽  
Light Conditions ◽  
Image Processing Algorithm ◽  
Ice Plant ◽  
Plant Leaf ◽  
Rgb Images

Abstract The productivity of horticultural crops in an artificial light condition are highly influenced by the structure of plant and the area coverage. Accurate measurement of leaf area is very important for predicting plant water demand and optimal growth. In this paper, we proposed an image processing algorithm to estimate the ice-plant leaf area from the RGB images under the artificial light condition. The images were taken using a digital camera and the RGB images were transformed to grayscale images. A binary masking was applied from a grayscale image by classifying each pixel, belonging to the region of interest from the background. Then the masked images were segmented and the leaf region was filled using region filling technique. Finally, the leaf area was calculated from the number of pixel and using known object area. The experiment was carried out in three different light conditions with same plant variety (Ice-plant, Mesembryanthemum crystallinum). The results showed that the correlation between the actual and measured leaf area was found over 0.97 (R2:0.973) by our proposed method. Different light condition also showed significant impact on plant growth. Our results inspired further research and development of algorithms for the specific applications.

scholarly journals A Noval Method for Detecting Plant Leaf Disease Using Image Processing and Deep Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 337-340
Author(s):  
M.Sowmiya, Et. al.
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Early Recognition ◽  
Conventional Technique ◽  
The Other ◽  
General Development ◽  
Plant Leaf ◽  
Ideal Approach ◽  
Over The Top

The venture presents a programmed approach for early illness and nourishment insufficiency identification in plant leaf. A great many dollars are being spent to shield the harvests every year. Creepy crawlies, sustenance lack, plant illness and vermin harm the harvests and, in this way, are hazardous for the general development of the yield. One strategy to ensure the harvest is early illness identification and nourishment lack so the yield can be secured. The most ideal approach to think about the soundness of the yield is the convenient assessment of the harvest. On the off chance that sickness or sustenance inadequacy are identified, fitting measures can be taken to shield the harvest from a major creation misfortune toward the end. Early recognition would be useful for limiting the use of the pesticides and would give direction to the determination of the pesticides. It has become a wide territory for research now a days and a great deal of examination has been completed worldwide for programmed location of illnesses. Conventional technique for assessment of the fields is unaided eye assessment however it is exceptionally hard to have a point by point assessment in enormous fields. To inspect the entire field, numerous human specialists are required which is over the top expensive and tedious. Thus a programmed framework is required which can inspect the harvests to distinguish invasion as well as can characterize the kind of sickness on crops. PC vision procedures give viable approaches to breaking down the pictures of leaves. CNN is utilized for order of pictures with and without ailment dependent on the picture highlights. This procedure is less difficult when contrasted with the other mechanized strategies and gives better outcomes

scholarly journals DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

2019 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.

Plant Leaf Disease Detection Using Image Processing

2021 ◽  
pp. 161-168
Author(s):  
M. Sahana ◽  
H. Reshma ◽  
R. Pavithra ◽  
B. S. Kavya
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 474
Author(s):  
Jun Wang ◽  
Liya Yu ◽  
Jing Yang ◽  
Hao Dong
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Detection Algorithms ◽  
Leaf Disease ◽  
Image Dataset

In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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