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Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables:State of the art and perspectives

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Artificial Intelligence in Agriculture 2023年第3期9卷 46-60页

作者： Sèton Calmette Ariane Houetohossou Vinasetan Ratheil Houndji Castro Gbêmêmali Hounmenou rachidatou sikirou Romain Lucas Glele Kakaï Laboratoire de Biomathématiques et d'Estimations Forestières Faculty of Agronomic SciencesUniversity of Abomey-Calavi04 BP 1525CotonouBenin Institut de Formation et de Recherche en Informatique University of Abomey-Calavi01 BP 526CotonouBenin Laboratoire de Défense des Cultures Centre de Recherches Agricoles d'AgonkanmeyInstitut National des Recherches Agricoles du Bénin(INRAB)01 BP 884CotonouBenin

Deep Learning(DL),a type of Machine Learning,has gained significant interest in many fields,including *** paper aims to shed light on deep learning techniques used in agriculture for abiotic and biotic stress detection in fruits and vegetables,their benefits,and the challenges faced by *** papers were collected from Web of Science,Scopus,Google Scholar,Springer,and Directory of Open Access Journals(DOAJ)using combinations of specific keywords such as:’Deep Learning’OR’Artificial Intelligence’in combination with fruit disease’,vegetable disease’,‘fruit stress',OR‘vegetable stress'following PRISMA *** the initial 818 papers identified using the keywords,132 were reviewed after excluding books,reviews,and the *** recovered scientific papers were from 2003 to 2022;93%addressed biotic stress on fruits and *** most common biotic stresses on species are fungal diseases(grey spots,brown spots,black spots,downy mildew,powdery mildew,and anthracnose).Few studies were interested in abiotic stresses(nutrient deficiency,water stress,light intensity,and heavy metal contamination).Deep Learning and Convolutional Neural Networks were the most used keywords,with GoogleNet(18.28%),ResNet50(16.67%),and VGG16(16.67%)as the most used ***-two percent of the data used to compile these models come from the fields,followed by data obtained *** problems due to unbalanced classes and the small size of some databases were also *** provided the research gaps and some perspectives from the reviewed *** research works are required for a deep understanding of the use of machine learning techniques in fruit and vegetable studies:collection of large datasets according to different scenarios on fruit and vegetable diseases,evaluation of the effect of climatic variability on the fruit and vegetable yield using AI methods and more abiotic stress studies.

关键词： Deep learning Prediction Fruits Vegetables Stress Agricultural yield

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Tomato Hybrid and Local Varieties Screened for Resistance to Bacterial Wilt Caused by <i>Ralstonia solanacearum</i>under Screen House and Field Conditions

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American Journal of Plant Sciences 2021年第8期12卷 1222-1235页

作者： Marie Epiphane Dossoumou rachidatou sikirou Appolinaire Adandonon Julienne Gonroudobou Lamine Baba-Moussa Laboratoire de Défense des Cultures (LDC) Centre de Recherches Agricoles d’Agonkanmey (CRA-Agonkanmey) Institut National des Recherches Agricoles du Bénin (INRAB) Cotonou Bénin Laboratoire de Biologie et de Typage Moléculaire en Microbiologie (LBTMM) Faculté des Sciences et Techniques Université d’Abomey-Calavi Cotonou Bénin Ecole de Gestion et de Production Végétale et Semencière (EGPVS) Université Nationale d’Agriculture (UNA) Porto-Novo Bénin

Bacterial wilt is the most damaging disease of tomato in Benin. To select resistant varieties of tomato against bacterial wilt, 21 tomato varieties were evaluated twice under screen house and field conditions in a randomized complete block design with four replicates and 21 treatments which are the tested varieties. Three weeks old tomato seedlings were inoculated by crown flooding with 40 ml of a bacterial suspension of R. solanacearum strain CR-1103-2, phylotype I sequvar 14. At 28 days after inoculation, the control resistant varieties Hawaii 7997 and PADMA maintained their resistance and the known susceptible variety Tohounvi showed to be susceptible to the bacterial wilt. Among the hybrid tomato varieties, only Cobra 26 was moderately resistant. No resistant line was found among the tested local varieties. The hybrid varieties Buffalo, Petomech, Tropimech, Sumo, Prado, Ninja, Jaguar, Anaya, Topaze, Cobra 34, Heinz, Kiara, Euclid and local Kêkêfo, Akikonkouin, Agbotrui and Adjaa were all susceptible. In conclusion, Cobra 26 emerges as a new variety of tomato moderately resistant to bacterial wilt. This is a new finding since it adds. All the tested varieties, regardless of their resistance, were colonized by R. solanacearum. This is a challenge for tomato farmers who must practice sanitation in their fields when producing tomato susceptible or resistant varieties. The variety Cobra 26 must be scaled up to farmers.

关键词： Solanaceae Diseases Varietal Resistance Control Method Lycopercicum esculentum Benin

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Effect of Different Plant Growth Promoting Rhizobacteria on Maize Seed Germination and Seedling Development

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American Journal of Plant Sciences 2013年第5期4卷 1013-1021页

作者： Pacome A.Noumavo Eméric Kochoni Yédéou O.Didagbé Adolphe Adjanohoun Marcellin Allagbé rachidatou sikirou Emma W.Gachomo Simeon O.Kotchoni Lamine Baba-Moussa Laboratoire de Biologie et de Typage Moléculaire en Microbiologie Département de Biochimie et de Biologie CellulaireUniversitéd’Abomey-CalaviCotonouBénin Centre de Recherches Agri-coles Sud Institut National des Recherches Agricoles du BéninAttogonBénin Laboratoire de Défense des Cultures Centre de Recherches Agricoles d’AgonkanmeyInstitut National des Re-cherches Agricoles du BéninCotonouBénin Department of Biology The State University of New JerseyCamdenUSA Center for Computational and Integrative Biology The State University of New JerseyCamdenUSA

Our study aimed at assessing the effects of 3 Plants Growth Promoting Rhizobacteria (PGPR) either singly or in combination on maize growth under laboratory and greenhouse conditions. Seeds were inoculated with single and combined solution of 108 CFU/ml of Rhizobacteria. Seeds were not inoculated for the control variant. The highest germination percentage was obtained with the combination of Pseudomonas fluorescens and Pseudomonas putida. This combination also recorded the best vigor index, plants circumferences number of leaves and the leaf area. The maximal heights of plants were observed with seeds treated with Azospirillum lipoferum with an increase of 37.32%. The highest rates of underground dry matter were recorded with A. lipoferum, with an increase of more than 56% comparative to control, while the combination P. fluorescens and P. putida increased the aerial dry matter of 59.11%. Finally, the highest value of the aerial biomass was obtained with the plants treated with the combination of P. fluorescens and P. putida and the highest underground biomass was obtained with plants treated only with A. lipoferum. These results suggest that specific combinations of PGPR can be considered as efficient alternative biofertilizers to promote maize seed germination, biomass and crop yield.

关键词： Azospirillum Pseudomonas PGPR Biofertlization Maize Seeds Germination Greenhouse

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