Survey on crop pest detection using deep learning and machine learning approaches

Abstract

The most important elements in the realm of commercial food standards are effective pest management and control. Crop pests can make a huge impact on crop quality and productivity. It is critical to seek and develop new tools to diagnose the pest disease before it caused major crop loss. Crop abnormalities, pests, or dietetic deficiencies have usually been diagnosed by human experts. Anyhow, this was both costly and time-consuming. To resolve these issues, some approaches for crop pest detection have to be focused on. A clear overview of recent research in the area of crop pests and pathogens identification using techniques in Machine Learning Techniques like Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), Naive Bayes (NB), and also some Deep Learning methods like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep convolutional neural network (DCNN), Deep Belief Network (DBN) was presented. The outlined strategy increases crop productivity while providing the highest level of crop protection. By offering the greatest amount of crop protection, the described strategy improves crop efficiency. This survey provides knowledge of some modern approaches for keeping an eye on agricultural fields for pest detection and contains a definition of plant pest detection to identify and categorise citrus plant pests, rice, and cotton as well as numerous ways of detecting them. These methods enable automatic monitoring of vast domains, therefore lowering human error and effort.

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

“…The absence of clear regulatory frameworks for AI applications in agriculture can hinder widespread adoption. Developing regulatory guidelines that ensure the responsible and ethical use of AI technologies is imperative [ 173 ].…”

Artificial neuronal networks are revolutionizing entomological research

“…In several studies, AI systems have been used to identify and track the spread of insect pests that can cause significant damage to crops and ecosystems (Aigner et al, 2016;Caselli & Petacchi, 2021;Chithambarathanu & Jeyakumar, 2023;Deka et al, 2022;He et al, 2019;Li et al, 2021;Liu et al, 2022;Xia et al, 2018;Zhao, Liu, et al, 2022;Zhao, Zhou, et al, 2022). For example, researchers have used AI to analyse satellite imagery to identify areas where pest outbreaks are occurring, providing an early warning and allowing for proactive management strategies (Gómez-Camperos et al, 2022;Meraj et al, 2022;Pourghasemi, 2021).…”

“…AI systems have demonstrated remarkable success in recognizing and identifying objects in images. By training AI to recognize the visual characteristics of different insect pests, such as their colour, shape, and size, these systems can be used to analyse images of crops and ecosystems and identify pests that are present (Azfar et al, 2023;Chithambarathanu & Jeyakumar, 2023;de Telmo & Rieder, 2020;Domingues et al, 2022;Li et al, 2021;Lima et al, 2020;Liu et al, 2019;Partel et al, 2019;Zhao, Liu, et al, 2022;Zhao, Zhou, et al, 2022). The training process for AI involves feeding them a large number of images of different pests and allowing them to learn the patterns and features that distinguish them from one another.…”

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

“…Tree pests, such as mole crickets, aphids, and Therioaphis maculata (Buckton) [2], detrimentally affect crop yields and forest ecosystems, underscoring the urgent need to devise sophisticated detection and management tactics. Trees, meanwhile, are pivotal to both the natural environment and human societies; they are foundational to ecosystems and crucial in sustaining biodiversity [3], climate regulation, soil and water conservation, and providing ecological services. Thus, the application of advanced technology to address arboricultural pest issues is imperative [4].…”