A review on the main challenges in automatic plant disease identification based on visible range images

An automated detection and classification of citrus plant diseases using image processing techniques: A review

The studies of plant trait/disease refer to the studies of visually observable patterns of a particular plant. Nowadays crops face many traits/diseases. Damage of the insect is one of the major trait/disease. Insecticides are not always proved efficient because insecticides may be toxic to some kind of birds. It also damages natural animal food chains. A common practice for plant scientists is to estimate the damage of plant (leaf, stem) because of disease by an eye on a scale based on percentage of affected area. It results in subjectivity and low throughput. This paper provides a advances in various methods used to study plant diseases/traits using image processing. The methods studied are for increasing throughput & reducing subjectiveness arising from human experts in detecting the plant diseases.

Advances in image processing for detection of plant diseases

The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images Several works utilized computer vision technologies effectively and contributed a lot in this domain This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions The survey would help researchers to gain understanding of computer vision applications in plant disease detection

Plants Disease Identification and Classification Through Leaf Images: A Survey

Fungi-caused diseases in sugarcane are the most predominant diseases which appear as spots on the leaves. If not treated on time, causes the severe loss. Excessive use of pesticide for plant diseases treatment increases the cost and environmental pollution so their use must be minimized. This can be achieved by targeting the diseases places, with the appropriate quantity and concentration of pesticide by estimating disease severity using image processing technique. Simple threshold and Triangle thresholding methods are used to segment the leaf area and lesion region area respectively. Finally diseases are catcogrise by calculating the quotient of lesion area and leaf area. The accuracy of the experiment is found to be 98.60 %. Research indicates that this method to calculate leaf disease severity is fast and accurate. Sugarcane being a long duration crop of 10 to 18 months, attacked by a number of diseases. Fungi- caused diseases in sugarcane are the most predominant diseases which appear as spots on the leaves. These spots prevent the vital process of photosynthesis to take place, hence to a large extent affects the growth and consequently the yield. In case of severe infection, the leaf becomes totally covered with spots. The various types of diseases on sugarcane determine the quality, quantity, and stability of yield. The diseases in sugarcane not only reduce the yield but also deteriorate of the variety and its withdrawal from cultivation.Excessive uses of pesticide for plant diseases treatment increases the danger of toxic residue level on agricultural products and has been identified as a major contributor to ground water contamination also pesticides are among the highest components in the production cost their use must be minimized. This can be achieved by estimating severity of disease and target the diseases places, with the appropriate quantity and concentration of pesticide.

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Leaf disease severity measurement using image processing

Soybean rust, caused by Phakopsora pachyrhizi, is one of the most destructive diseases for soybean production. It often causes significant yield loss and may rapidly spread from field to field through airborne urediniospores. In order to implement timely fungicide treatments for the most effective control of the disease, it is essential to detect the infection and severity of soybean rust. This research explored feasible methods for detecting soybean rust and quantifying severity. In this study, images of soybean leaves with different rust severity were collected using both a portable spectroradiometer and a multispectral CDD camera. Different forms of vegetation indices were used to investigate the possibility of detecting rust infection. Results indicated that both leaf development stage and rust infection severity changed the surface reflectance within a wide band of spectrum. In general, old leaves with most severe rust infection resulted in lowest reflectance. A difference vegetation index (DVI) showed a positive correlation with reflectance differences. However, it lacks solid evidence to identify such reflectance change was solely caused by rust. As an alternative, three parameters, i.e. ratio of infected area (RIA), lesion color index (LCI) and rust severity index (RSI), were extracted from the multispectral images and used to detect leaf infection and severity of infection. The preliminary results obtained from this laboratory-scale research demonstrated that this multispectral imaging method could quantitatively detect soybean rust. Further tests of field scale are needed to verify the effectiveness and reliability of this sensing method to detect and quantify soybean rust infection in real time field scouting.

Qin Zhang

Papers

Detection of soybean rust using a multispectral image sensor