Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners.

Brassica napus (canola/oilseed rape/rapeseed) is an economically important crop, mostly found in temperate and sub-tropical regions, that is cultivated widely for its edible oil. Major diseases of Brassica crops such as Blackleg, Clubroot, Sclerotinia Stem Rot, Downy Mildew, Alternaria Leaf Spot and White Rust have caused significant yield and economic losses in rapeseed-producing countries worldwide, exacerbated by global climate change, and, if not remedied effectively, will threaten global food security. To gain further insights into the host–pathogen interactions in relation to Brassica diseases, it is critical that we review current knowledge in this area and discuss how omics technologies can offer promising results and help to push boundaries in our understanding of the resistance mechanisms. Omics technologies, such as genomics, proteomics, transcriptomics and metabolomics approaches, allow us to understand the host and pathogen, as well as the interaction between the two species at a deeper level. With these integrated data in multi-omics and systems biology, we are able to breed high-quality disease-resistant Brassica crops in a more holistic, targeted and accurate way.

Since their domestication, Brassica oilseed species have undergone progressive transformation allied with the development of breeding and molecular technologies. The canola (Brassica napus) crop has rapidly expanded globally in the last 30 years with intensive innovations in canola varieties, providing for a wider range of markets apart from the food industry. The breeding efforts of B. napus, the main source of canola oil and canola meal, have been mainly focused on improving seed yield, oil quality, and meal quality along with disease resistance, abiotic stress tolerance, and herbicide resistance. The revolution in genetics and gene technologies, including genetic mapping, molecular markers, genomic tools, and gene technology, especially gene editing tools, has allowed an understanding of the complex genetic makeup and gene functions in the major bioprocesses of the Brassicales, especially Brassica oil crops. Here, we provide an overview on the contributions of these technologies in improving the major traits of B. napus and discuss their potential use to accomplish new improvement targets.

Plant disease outbreaks continuously challenge food security and sustainability. Traditional chemical methods used to treat diseases have environmental and health concerns, raising the need to enhance inherent plant disease resistance mechanisms. Traits, including disease resistance, can be linked to specific loci in the genome and identifying these markers facilitates targeted breeding approaches. Several methods, including genome‐wide association studies and genomic selection, have been used to identify important markers and select varieties with desirable traits. However, these traditional approaches may not fully capture the non‐linear characteristics of the effect of genomic variation on traits. Machine learning, known for its data‐mining abilities, offers an opportunity to enhance the accuracy of the existing trait association approaches. It has found applications in predicting various agronomic traits across several species. However, its use in disease resistance prediction remains limited. This review highlights the potential of machine learning as a complementary tool for predicting the genetic loci contributing to pathogen resistance. We provide an overview of traditional trait prediction methods, summarize machine‐learning applications, and address the challenges and opportunities associated with machine learning‐based crop disease resistance prediction.

Vegetable crops are of immense importance due to their nutritional value ( Slavin and Lloyd 2012), environmental sustainability ( Parajuli et al. 2019), contribution to food security ( Bokelmann et al. 2022), and economic and cultural significance ( Tonini et al. 2022). They are found in several plant families and are cultivated for their edible flower buds, fruit, leaves, stems, and tubers. The diversity of vegetable crops is no better illustrated than by Brassica rapa and B. oleracea, 2 species whose many morphotypes account for more than 30 household vegetables, including broccoli, bok choy, cabbage, and cauliflower, among many others. The major vegetable crops include potato, tomato, cucumber, eggplant, cabbage, lettuce, and broccoli ( FAO 2023). The worldwide production of vegetables was 1.15 billion metric tons in 2021, with countries such as China, India, the United States, Turkey, and Iran contributing significant yields ( FAO 2023). Among the many challenges facing vegetable growers, diseases caused by microbes pose a serious threat to both crop yield and quality ( Richard et al. 2022). Disease-related yield loss in vegetables is considerable, with an estimated loss of more than 20% in major crops like potato ( Savary et al. 2019) and reports as high as 40% in other vegetables ( Gupta and Thind 2018). The diversity of vegetable-infecting pathogens is extensive, with numerous types of bacteria, fungi, oomycetes, protists, and viruses responsible for disease ( Supplementary Table S1 ). These pathogens have a profound impact on virtually all aspects of plant health, including interfering with physiological processes, hindering the uptake of nutrients, and weakening structures ( Baligar et al. 2007; Yang and Luo 2021). As a result, the identification of naturally occurring genetic resistance has been an important objective among researchers.