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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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