Hey Ananias Nakale,

kindly, check through these papers, it will give you a clear star:

https://link.springer.com/article/10.3103/S0146411622030038

Article Edge AI-IoT Pivot Irrigation, Plant Diseases and Pests Identification

Article Artificial intelligence in pest insect monitoring

Article Automation and digitization of agriculture using artificial ...

https://www.indianjournals.com/ijor.aspx?target=ijor:ar&volume=37&issue=4&article=006

https://www.hindawi.com/journals/jfq/2021/5584754/

Hope it helps

Ananias Nakale

A literature review on pest identification research project using AI could include the following points:

- Pest identification is a crucial task for achieving smart agriculture and optimizing agricultural resources, as insect pests can cause significant losses to crops and farmers¹².

- AI techniques such as deep learning, machine learning, and computer vision can help to automate the process of pest detection and identification, by using various sensors, cameras, and drones to collect and analyze data from the field¹²³⁴.

Some of the challenges and opportunities for pest identification using AI are:

- The lack of large and diverse datasets for pest detection and identification, which requires data augmentation methods or transfer learning techniques to improve the performance of AI models¹⁴.

- The need for accurate and robust AI models that can handle different environmental conditions, crop types, pest species, and image qualities¹²⁴.

- The potential for integrating AI models with other technologies such as wireless sensor networks, internet of things, cloud computing, and blockchain to enable real-time monitoring, communication, and decision making for pest control²³⁴.

- The ethical and social implications of using AI for pest identification, such as the privacy and security of data, the trust and acceptance of farmers, and the impact on biodiversity and ecosystems⁵.

Some of the existing or ongoing research projects on pest identification using AI are:

- Insect Pest Detection and Identification Method Based on Deep Learning for Realizing a Pest Control System: This project proposes a two-stage detection and identification method for small insect pests based on convolutional neural networks (CNNs), using YOLOv3 for region proposal network and Xception for re-identification model¹.

- An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks: This project proposes a method for pest monitoring using wireless sensor networks by recognizing insect behavior using various sensors, and using artificial neural networks (ANNs) for pest detection and classification².

- PlantVillage Nuru: Pest and Disease Monitoring Using Artificial Intelligence: This project aims to transform pest and disease monitoring by using AI, advanced sensor technology, and crowdsourcing to connect the global agricultural community and help smallholder farmers³.

- An Automated Pest Identification and Classification in Crops Using Deep Learning: This project proposes a method for pest identification and classification in crops using deep learning, by using Faster R-CNN for object detection and ResNet-50 for feature extraction⁴.

- Artificial Intelligence in Integrated Pest Management: This project reviews the current state-of-the-art of AI applications in integrated pest management (IPM), such as pest detection, diagnosis, forecasting, decision support, and precision spraying⁵.

Sources:

(1) Insect Pest Detection and Identification Method Based on Deep Learning .... https://ieeexplore.ieee.org/document/9240458.

(2) An Artificial Neural Network-Based Pest Identification and ... - Hindawi. https://www.hindawi.com/journals/jfq/2022/5801206/.

(3) PlantVillage Nuru: Pest and disease monitoring using artificial .... https://bigdata.cgiar.org/inspire/inspire-challenge-2017/pest-and-disease-monitoring-by-using-artificial-intelligence/.

(4) An Automated Pest Identification and Classification in Crops Using .... https://link.springer.com/article/10.3103/S0146411622030038.

(5) Artificial Intelligence in Integrated Pest Management. https://www.researchgate.net/publication/348126718_Artificial_Intelligence_in_Integrated_Pest_Management.