scholarly journals Plant leaf Damage Detection using Segmentation

A primary source of livelihood is agriculture. In developing country like India, wide-ranging employment opportunities are provided by Agriculture for the villagers. Various crops are included in the agricultural system of India and 70% of the population depends upon agriculture as reported by survey. Because of lagging in technical knowledge, manual cultivation is adopted by majority of the Indian farmers. The kind of crops that grows well on their land is unaware by the farmers. The agriculture production is affected by the heterogeneous diseases that affect the plant leaves and result in the productive loss. Moreover, the quality as well as quantity of the agricultural production is reduced by it. A key role is played by the leaves in the rapid growth of the plants and production of crops. The identification of diseases related to plant leaf is a difficult task for the farmers and for the researchers. At present, various pesticides were sprayed on the plants that directly or indirectly affect the human health and the economy. Various methods must be adopted for detecting these kinds of plant diseases. This paper presents a review of various plant diseases and several advanced technologies in detecting the diseases.

Author(s):  
Shivangi Singh

Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, also reduction in both quality and quantity of agricultural production. Leaves are important for fast growing of plant and to extend production of crops. Identifying diseases in plant leaves is challenging for farmers and also for researchers. Currently farmers are spraying pesticides to the plants but it affects humans directly or indirectly by health or also economically. To detect these plant diseases many fast techniques got to be adopt. In this paper, we have done surveys on different leaf diseases and various advanced techniques to detect these diseases. As said by Mahatma Gandhi, "Agriculture is the backbone of the Indian Economy". Hence the detection of leaf diseases is an important aspect in increasing the yield of a crop. By detecting the leaf disease farmer can increase the crop yield which leads in growth of country’s economy.


2021 ◽  
Vol 3 (3) ◽  
pp. 478-493
Author(s):  
Ahmed Abdelmoamen Ahmed ◽  
Gopireddy Harshavardhan Reddy

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.


Agricultural productive is the dominant issue, which affects the economy of the country excessively. So detection of diseases in plants plays a major role in Agricultural field. In previous day’s farmers in the fields used to observe the plants just by seeing with their eye for identification of a disease. But this method may take lot of time, expensive and inaccurate. So advanced technology that can identify plant diseases as easily as possible is needed, in order to decrease the percentage rate of the contamination of crops and increase the fertility. Here in this paper techniques like preprocessing, segmentation and classification of image are used. Here Tomato, Maize, Grape, Potato and Apple plant leaves are used, where different diseases are identified for each plant. For Classification we used Convolution Neural Network Algorithm, so that we can automatically detect the plant leaf diseases. And this will help farmers to identify their diseases as early as possible.


2015 ◽  
Vol 8 (9) ◽  
pp. 71
Author(s):  
Olawale Emmanuel Olayide ◽  
Isaac Kow Tetteh ◽  
Labode Popoola

This paper analysed policy correlates of agricultural production and agricultural production sustainability outcomes in Ghana and Nigeria. It underscores the influence of political systems and international development agendas as correlates of agricultural production and agricultural production sustainability outcomes. This is to the extent of providing evidence policy on agricultural production and agricultural production sustainability outcomes. Ghana and Nigeria have comparable farming/agricultural system and policy environment. Data used for the analyses spanned five decades. Trends analysis and inferential statistics were employed. The results revealed that policy correlates can contribute to the current discourse in sustainable development agenda and to resolving the dilemma of agricultural policy implementation for sustainable agricultural development, especially in Ghana and Nigeria. The findings reinforce the need for appropriate policies in transforming the agricultural sector while ensuring sustainable development outcomes.


2020 ◽  
Vol 96 (6) ◽  
Author(s):  
A Katsoula ◽  
S Vasileiadis ◽  
M Sapountzi ◽  
Dimitrios G Karpouzas

ABSTRACT Pesticides interact with microorganisms in various ways with the outcome being negative or positive for the soil microbiota. Pesticides' effects on soil microorganisms have been studied extensively in soil but not in other pesticides-exposed microbial habitats like the phyllosphere. We tested the hypothesis that soil and phyllosphere support distinct microbial communities, but exhibit a similar response (accelerated biodegradation or toxicity) to repeated exposure to the fungicide iprodione. Pepper plants received four repeated foliage or soil applications of iprodione, which accelerated its degradation in soil (DT50_1st = 1.23 and DT50_4th = 0.48 days) and on plant leaves (DT50_1st > 365 and DT50_4th = 5.95 days). The composition of the epiphytic and soil bacterial and fungal communities, determined by amplicon sequencing, was significantly altered by iprodione. The archaeal epiphytic and soil communities responded differently; the former showed no response to iprodione. Three iprodione-degrading Paenarthrobacter strains were isolated from soil and phyllosphere. They hydrolyzed iprodione to 3,5-dichloraniline via the formation of 3,5-dichlorophenyl-carboxiamide and 3,5-dichlorophenylurea-acetate, a pathway shared by other soil-derived arthrobacters implying a phylogenetic specialization in iprodione biotransformation. Our results suggest that iprodione-repeated application could affect soil and epiphytic microbial communities with implications for the homeostasis of the plant–soil system and agricultural production.


Author(s):  
Meghashree ◽  
Alwyn Edison Mendonca ◽  
Ashika S Shetty

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.


Author(s):  
Aneel Narayanapur ◽  
Pavankumar Naik ◽  
Priya B Kori ◽  
Naseem Kalaburgi ◽  
Rubiya I M ◽  
...  

The detection of plant leaf is an very important factor to prevent serious outbreak. Automatic detection of plant disease is essential research topic. Most plant diseases are caused by fungi, bacteria, and viruses. Fungi are identified primarily from their morphology, with emphasis placed on their reproductive structures. Bacteria are considered more primitive than fungi and generally have simpler life cycles. With few exceptions, bacteria exist as single cells and increase in numbers by dividing into two cells during a process called binary fission Viruses are extremely tiny particles consisting of protein and genetic material with no associated protein. The term disease is usually used only for the destruction of live plants. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, this RGB is converted to HSI because RGB is for color generation and his for color descriptor. Then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. from SGDM matrices. Finally the presence of diseases on the plant leaf is evaluated.


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.


Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.


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.


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