Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Cruz, Albert C.; Ampatzidis, Yiannis; Pierro, Roberto; Materazzi, Alberto; Panattoni, Alessandra; Bellis, Luigi De; Luvisi, Andrea

doi:10.1016/j.compag.2018.12.028

Cited by 142 publications

(67 citation statements)

References 29 publications

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“…However, most studies focus on white-berried grape cultivars, which have different symptoms (yellow and chlorotic leaves) from red-berried grape cultivars (purple-reddish leaves).The aim of this study was to investigate the effect of BN disease on the primary and secondary metabolism parameters in leaves of a yet untested red-berried grape cv. Sangiovese, which is one of the most widespread Italian cultivars whose susceptibility to BN has been reported [22,23]. Specifically, the sugar accumulation, photosynthetic pigments and the compounds of phenylpropanoid pathways, such as phenolic compounds, flavonoids, proanthocyanidins, anthocyanins, and lignin, and their respective amounts were evaluated in BN-positive and BN-negative leaves, in two periods, according to symptom appearance (asymptomatic or symptomatic).…”

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confidence: 99%

Biochemical Changes in Leaves of Vitis vinifera cv. Sangiovese Infected by Bois Noir Phytoplasma

et al. 2020

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Bois noir is a disease associated with the presence of phytoplasma 'Candidatus Phytoplasma solani' belonging to the Stolbur group (subgroup 16SrXII-A), which has a heavy economic impact on grapevines. This study focused on the changes induced by phytoplasma in terms of the profile and amount of secondary metabolites synthesized in the phenylpropanoid pathway in leaves of Vitis vinifera L. red-berried cultivar Sangiovese. Metabolic alterations were assessed according to the disease progression through measurements of soluble sugars, chlorophyll, and phenolic compounds produced by plant hosts, in response to disease on symptomatic and asymptomatic Bois noir-positive plants. Significant differences were revealed in the amount of soluble sugars, chlorophyll, and accumulation/reduction of some compounds synthesized in the phenylpropanoid pathway of Bois noir-positive and negative grapevine leaves. Our results showed a marked increase in phenolic and flavonoid production and a parallel decrease in lignin content in Bois noir-positive compared to negative leaves. Interestingly, some parameters (chlorophyll a, soluble sugars, total phenolic or flavonoids content, proanthocyanidins, quercetin) differed between Bois noir-positive and negative leaves regardless of symptoms, indicating measurable biochemical changes in asymptomatic leaves. Our grapevine cultivar Sangiovese results highlighted an extensive modulation of the phenylpropanoid biosynthetic pathway as a defense mechanism activated by the host plant in response to Bois noir disease.Pathogens 2020, 9, 269 2 of 17 This strain is transmitted from plant-to-plant above all by Hyalesthes obsoletus Signoret (Homoptera: Cixiidae), a polyphagous leafhopper, which has various hosts that act as phytoplasma reservoirs [5].The symptoms on infected plants depend on the variety, but generally, different visual symptoms are exhibited, such as stunting, abnormal lignification of canes, short internodes, flower abortion, downward curling, and yellowing and/or reddening leaves, resulting in severe yield reduction and even plant death [6].A number of mechanisms by which phytoplasmas induce disease symptoms have been proposed: through the production of phytoplasma-secreted proteins (effectors), which play an important role in host-pathogen interactions and pathogenicity eliciting the disease symptoms [7]. The latter include yellowing or reddening leaves, in relation to white-or red-berried grape varieties respectively, induced by phytoplasma are associated with an alteration in the use of sugars in the phloem [8,9]. In fact, several studies have reported evidence of extensive modification in the synthesis and transport of soluble carbohydrates and starch, a decrease in photosynthetic activity, the breakdown of chlorophyll, carotenoids, and their biosynthesis inhibition in many phytoplasma-infected plants, including in Vitis spp. [8,[10][11][12][13]. In BN-infected grapevine of white-berried cv. Chardonnay, symptoms have been related to reduced photosynthetic activity and the anomalous ...

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Biochemical Changes in Leaves of Vitis vinifera cv. Sangiovese Infected by Bois Noir Phytoplasma

et al. 2020

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“…e feature extraction of plant disease faces many problems in identifying plant disease. e distinct image features include textures, shape, color, and motion-related attributes, which are the essential conditions for disease feature extraction [21,22]. Raza and his colleagues described a method that uses color and texture features to extract disease spots [23].…”

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confidence: 99%

Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming

Guo

Zhang

Yin³

et al. 2020

Discrete Dynamics in Nature and Society

134

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The identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. The results show that the accuracy of the method is 83.57%, which is better than the traditional method, thus reducing the influence of disease on agricultural production and being favorable to sustainable development of agriculture. Therefore, the deep learning algorithm proposed in the paper is of great significance in intelligent agriculture, ecological protection, and agricultural production.

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“…Coulibaly et al [26] proposed an approach for the identification of mildew disease in pearl millet, which is using transfer learning with feature extraction. Cruz et al [27] proposed an artificial intelligence-based approach for detecting grapevine yellows symptoms. Deep convolutional neural network-based approach for crop disease classification on wheat images proposed by Picon et al [28].…”

Section: A Disease Detectionmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Cited by 142 publications

References 29 publications

Biochemical Changes in Leaves of Vitis vinifera cv. Sangiovese Infected by Bois Noir Phytoplasma

Biochemical Changes in Leaves of Vitis vinifera cv. Sangiovese Infected by Bois Noir Phytoplasma

Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

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