Problems, challenges and future of plant disease management: from an ecological point of view

Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; （ii） reducing production potential in agriculture due to competition for land in fertile areas and exhaustion of marginal arable lands; （iii） deteriorating ecology of agro-ecosystems and depletion of natural resources; and （iv） increased risk of disease epidemics resulting from agricultural intensification and monocultures. Future plant disease management should aim to strengthen food security for a stable society while simultaneously safeguarding the health of associated ecosystems and reducing dependency on natural resources. To achieve these multiple functionalities, sustainable plant disease management should place emphases on rational adaptation of resistance, avoidance, elimination and remediation strategies individually and collectively, guided by traits of specific host-pathogen associations using evolutionary ecology principles to create environmental （biotic and abiotic） conditions favorable for host growth and development while adverse to pathogen reproduction and evolution.

Triple bottom-line consideration of sustainable plant disease management:From economic,sociological and ecological perspectives

Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecological resilience.However,technologies available are often limited due to different interests between producers and society and lacks of proper understanding of economic thresholds and the complex interactions among ecology,productivity and profitability.A comprehensive synergy and conflict evaluation of economic,sociological and ecological effects with technologies,productions and evolutionary principles as main components should be used to guide sustainable disease management that aims to mitigate crop and economic losses in the short term while maintaining functional farm ecosystem in the long term.Consequently,there should be an increased emphasis on technology development,public education and information exchange among governments,researchers,producers and consumers to broaden the options for disease management in the future.

Cross-comparative review of Machine learning for plant disease detection:apple,cassava,cotton and potato plants

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food security.Detecting these diseases early can help mitigate their impact and ensure healthy crop yields.Machine learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected crops.These algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant dis-eases.Besides early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many more.Over the years,research has focused on using machine-learning algorithms for plant disease detection.Nevertheless,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease management.In view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and potato.We conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the years.After filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered relevant.From these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected plants.Results from our re-view show that deep learning and other algorithms performed significantly well in detecting plant diseases.In addition,we found a few references to plant disease management covering prevention,diagnosis,control,and monitoring.In view of this,little or no work has explored the prediction of the recovery of affected plants.Hence,we propose opportunities for developing machine learning-based technologies to cover prevention,diag-nosis,control,monitoring,and recovery.