In the rapidly evolving field of artificial intelligence (AI), the
application of large language models (LLMs) in agriculture, particularly in
pest management, remains nascent. We aimed to prove the feasibility by
evaluating the content of the pest management advice generated by LLMs,
including the Generative Pre-trained Transformer (GPT) series from OpenAI and
the FLAN series from Google. Considering the context-specific properties of
agricultural advice, automatically measuring or quantifying the quality of text
generated by LLMs becomes a significant challenge. We proposed an innovative
approach, using GPT-4 as an evaluator, to score the generated content on
Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and
Exhaustiveness. Additionally, we integrated an expert system based on crop
threshold data as a baseline to obtain scores for Factual Accuracy on whether
pests found in crop fields should take management action. Each model's score
was weighted by percentage to obtain a final score. The results showed that
GPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.
Furthermore, the use of instruction-based prompting containing domain-specific
knowledge proved the feasibility of LLMs as an effective tool in agriculture,
with an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing
pest management suggestions.

通过对农业领域（特别是害虫管理）中人工智能大型语言模型（LLMs）生成的害虫管理建议内容进行评估，证明了人工智能大型语言模型在农业中的可行性，并且通过创新方法使用 GPT-4 作为评估器，在相关领域专家系统的基础上，综合评分，显示出 GPT-3.4 和 GPT-4 在大多数评估类别中优于 FLAN 模型，验证了人工智能大型语言模型在提供农业害虫管理建议方面的有效性和准确性（准确率为 72%）。

GPT-4 作为评估器：在农业中评估大型语言模型的农害管理

GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management  in Agriculture

This paper presents a dataset of agricultural pest images captured over five
years by thousands of small holder farmers and farming extension workers across
India. The dataset has been used to support a mobile application that relies on
artificial intelligence to assist farmers with pest management decisions.
Creation came from a mix of organized data collection, and from mobile
application usage that was less controlled. This makes the dataset unique
within the pest detection community, exhibiting a number of characteristics
that place it closer to other non-agricultural objected detection datasets.
This not only makes the dataset applicable to future pest management
applications, it opens the door for a wide variety of other research agendas.

本文介绍了一个由数千名小农户和农业推广工作者在印度拍摄了五年的农业害虫图像数据集，该数据集被用于支持一款基于人工智能的移动应用程序，以协助农民进行害虫管理决策。

BOLLWM：印度棉田棉铃虫害监测的真实世界数据集

BOLLWM: A real-world dataset for bollworm pest monitoring from cotton  fields in India

Early quantification of Tuta absoluta pest's effects in tomato plants is a
very important factor in controlling and preventing serious damages of the
pest. The invasion of Tuta absoluta is considered a major threat to tomato
production causing heavy loss ranging from 80 to 100 percent when not properly
managed. Therefore, real-time and early quantification of tomato leaf miner
Tuta absoluta, can play an important role in addressing the issue of pest
management and enhance farmers' decisions. In this study, we propose a
Convolutional Neural Network (CNN) approach in determining the effects of Tuta
absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19,
ResNet and Inception-V3) were used in training classifiers on a dataset
containing health and infested tomato leaves collected from real field
experiments. Among the pre-trained architectures, experimental results showed
that Inception-V3 yielded the best results with an average accuracy of 87.2
percent in estimating the severity status of Tuta absoluta in tomato plants.
The pre-trained models could also easily identify High Tuta severity status
compared to other severity status (Low tuta and No tuta)

本研究提出了使用卷积神经网络通过对受到卫星夜蛾感染的番茄叶进行分类来确定卫星夜蛾对番茄植物的影响程度，结果显示 Inception-V3 模型的平均准确率达到 87.2％，可以有效地识别高等级的卫星夜蛾感染程度。