[26] | 2020 | Mango sorting and grading according to their quality. | SqueezeNet deep CNN. | Nearly 10.000 mango fruits self-collected using smart phone, normal camera and a SEEK Thermal camera. | RGB images: 93.33%; Thermal images: 92.27% |
[15] | 2021 | Metection of mangoes infected with anthracnose. | proposed CNN based on AlexNet architecture. | Dataset of images captured by a CDD camera in real conditions. | 70% |
[9] | 2021 | Segment and identify the diseases (Anthracnose and apical nacrosis) on the mango leave. | CNN based Fully-convolutional- network (FrCNnet) model. | Real-time self-collected 2286 images captured using different types of image capturing gadgets. | Accuracy: 99.2%; FNR: 0.8% |
[24] | 2021 | Mango and grape diseases detection and identification. | AlexNet CNN. | 7,222 and 1,266 images of diseased and healthy mango and grape leaves collected from the PlantVillage dataset and acquired locally respectively. | Grape: 99% Mango: 89% |
[17] | 2022 | Mango leaf disease identification and classification. | CNN with crossover-based levy flight distribution, MobileNetV2 model and SVM. | 380 mango images self-captured from a mango cultivating land in India. | Not specified |