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

• Published in: Artificial intelligence in agriculture Vol. 9; pp. 46 - 60
• Main Authors: Houetohossou, Sèton Calmette Ariane, Houndji, Vinasetan Ratheil, Hounmenou, Castro Gbêmêmali, Sikirou, Rachidatou, Kakaï, Romain Lucas Glele
• Format: Journal Article
• Language: English
• Published: KeAi Communications Co., Ltd 01.09.2023
• Subjects: Agricultural yield; Deep learning; Fruits; Prediction; Stress; Vegetables
• ISSN: 2589-7217; 2589-7217
• DOI: 10.1016/j.aiia.2023.08.001

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many fields, including agriculture. This 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 users. Scientific 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 guidelines. From the initial 818 papers identified using the keywords, 132 were reviewed after excluding books, reviews, and the irrelevant. The recovered scientific papers were from 2003 to 2022; 93 % addressed biotic stress on fruits and vegetables. The 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 architectures. Fifty-two percent of the data used to compile these models come from the fields, followed by data obtained online. Precision problems due to unbalanced classes and the small size of some databases were also analyzed. We provided the research gaps and some perspectives from the reviewed papers. Further 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.