Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset

Abstract

Kiwifruits are highly valued for their nutritional and health-related benefits as well as for their economic importance, since they significantly contribute to the economy of many countries that cultivate them. However, kiwifruits are very sensitive to diseases that may substantially impact their final quantity and quality. Computer vision (CV) has been extensively employed for disease recognition in the agricultural sector within the last decade; yet there are limited works dealing with kiwifruit disease recognition, and there is an obvious lack of open datasets to promote relevant research, especially when compared to research on other cultivations, e.g., grapes. To this end, this study introduces the first-reported open dataset for kiwifruit leaf disease recognition, including Alternaria, Nematodes and Phytophthora, while image datasets of Nematodes have not been previously reported. The proposed dataset, named AgriVision-Kiwi Dataset, has been used first for leaf detection with You Only Look Once version 11 (YOLOv11), reporting a bounding box loss of 0.053, and then to train various deep learning models for kiwifruit diseases recognition, reporting accuracies of 98.80% ± 0.5, e.g., 98.30% to 99.30%, after 10-fold cross-validation. The introduced dataset aims to encourage the development of CV applications towards the timely prevention of diseases’ spreading.