A Critical Review of Whitefly (Bemisia tabaci gennadius) Cryptic Species Associated with the Cotton Leaf Curl Disease

Afzal, Muhammad; Saeed, Shafqat; Riaz, Hasan; Ishtiaq, Muhammad; Rahman, M. H.

doi:10.17582/journal.jis/2023/9.1.24.43

Cited by 1 publication

(1 citation statement)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The priorities may be determined by the worldwide economic losses posed by the individual type of pest. For instance, whiteflies have been reported in Central America, North America, South America, China, Central Africa, South Africa, South East Asia, Pakistan, and India [70,71]. Similarly, cotton mealybugs have been reported in more than 26 countries in various ecological zones [72].…”

Section: Urgent Need For Iot Towards Worldwide Major Cotton Pestsmentioning

confidence: 99%

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton

Kiobia

Mwitta

Fue

et al. 2023

Sensors

View full text Add to dashboard Cite

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system’s detection accuracy.

show abstract

Section: Urgent Need For Iot Towards Worldwide Major Cotton Pestsmentioning

confidence: 99%