Effective pest management is crucial for enhancing agricultural productivity, especially for crops such as sugarcane and wheat that are highly vulnerable to pest infestations. Traditional pest management methods depend heavily on manual field inspections and the use of chemical pesticides. These approaches are often costly, time-consuming, labor-intensive, and can have a negative impact on the environment. To overcome these challenges, this study presents a lightweight framework for pest detection and pesticide recommendation, designed for low-resource devices such as smartphones and drones, making it suitable for use by small and marginal farmers. The proposed framework includes two main components. The first is a Pest Detection Module that uses a compact, lightweight convolutional neural network (CNN) combined with prototypical meta-learning to accurately identify pests even when only a few training samples are available. The second is a Pesticide Recommendation Module that incorporates environmental factors like crop type and growth stage to suggest safe and eco-friendly pesticide recommendations. To train and evaluate our framework, a comprehensive pest image dataset was developed by combining multiple publicly available datasets. The final dataset contains samples with different viewing angles, pest sizes, and background conditions to ensure strong generalization. Experimental results show that the proposed lightweight CNN achieves high accuracy, comparable to state-of-the-art models, while significantly reducing computational complexity. The Decision Support System additionally improves pest management by reducing dependence on traditional chemical pesticides and encouraging sustainable practices, demonstrating its potential for real-time applications in precision agriculture.

翻译：有效的害虫管理对于提高农业生产力至关重要，尤其是对于甘蔗和小麦等极易受虫害侵袭的作物。传统的害虫管理方法严重依赖人工田间巡查和化学农药的使用。这些方法通常成本高昂、耗时费力、劳动密集，且可能对环境产生负面影响。为克服这些挑战，本研究提出了一种用于害虫检测与农药推荐的轻量级框架，该框架专为智能手机和无人机等低资源设备设计，使其适合小型及边缘农户使用。所提出的框架包含两个主要组成部分。其一是害虫检测模块，该模块采用紧凑轻量的卷积神经网络（CNN）结合原型元学习，即使在仅有少量训练样本的情况下也能准确识别害虫。其二是农药推荐模块，该模块整合了作物类型和生长阶段等环境因素，以推荐安全且环保的农药方案。为训练和评估我们的框架，通过整合多个公开可用的数据集，构建了一个全面的害虫图像数据集。最终数据集包含具有不同视角、害虫尺寸和背景条件的样本，以确保强大的泛化能力。实验结果表明，所提出的轻量级CNN在显著降低计算复杂度的同时，实现了与最先进模型相媲美的高精度。该决策支持系统通过减少对传统化学农药的依赖并鼓励可持续实践，进一步改善了害虫管理，展现了其在精准农业中实时应用的潜力。