The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

利用可见-近红外光谱分析技术可以准确快速的获取土壤养分含量, 但不同类型土壤间养分含量校正模型的普适性是亟待解决的关键问题。 为提高有机质含量光谱校正模型在多类型土壤之间的普适性和农田在线检测有机质含量速度, 利用美国M107B区66个样品建立基于可见-近红外光谱的土壤有机质含量的粒子群-最小二乘支持向量机(PSO-LSSVM)校正模型, 预测M107B区的23个验证集样品的决定系数R²=0.859, 相对分析误差RPD=2.660; 将M107B区89个土壤样品作为校正集建模后对N116B区20个验证集样品的有机质含量预测, 预测R²=0.562, 预测RPD=0.952, 模型的预测R²和预测RPD分别降低34.6%和64.2%, 表明M107B区土壤有机质含量的可见-近红外光谱校正模型直接用于N116B区时, 预测精度显著降低; 将N116B区部分土壤样品加入到M107B区样品集后重新建模, 并预测N116B区20个验证集样品的有机质含量, 当加入的N116B区土壤样品数量达到35以上, 预测R²>0.80, 预测RPD>2.0; 加入到校正集的N116B区土壤样品数量从0增加到50, 模型预测R²从0.562增加到0.811, 预测RPD从0.952增加到2.274, 精度逐渐提高。 结果表明, 在M107B区校正模型中加入N116B区部分土壤样品建模, 能够有效提高M107B区土壤校正模型对N116B区土壤有机质含量的预测精度; 加入的N116B区土壤样品数量达到50以上, 模型预测性能趋于稳定, 预测精度达到实用要求, 成功将M107B区土壤有机质含量校正模型传递给N116B区土壤; 优先选择与M107B区土壤样品的有机质含量或光谱曲线差异较大的N116B区土壤样品参与建模, 可有效避免模型传递时模型性能出现突变。 提出的方法能够有效提高M107B区土壤的有机质校正模型对N116B区土壤的预测精度, 为基于可见-近红外光谱的农田土壤有机质含量实时检测提供一种新的经济可行的模型传递方法, 为提高多类型土壤的有机质含量检测模型的普适性提供一种有效的解决方案。

针对农作物病、 虫、 草害化学防治时对靶变量施药以减少农药使用量、 提高农药利用率的需求, 本文研究了基于荧光光谱信息和主动光源方法在不同环境下探测绿色植物的方法。 通过白色、 蓝色和红色LED主动光源照射样本, 采集了白天室内自然光照、 白天太阳直射、 白天无太阳直射和夜晚黑暗环境四种场景下的绿色植物和非绿色植物样本光谱。 首先基于多波段光谱信息建立簇类独立软模式法(SIMCA)和线性判别分析(LDA)模型, 验证利用主动光源照射下绿色植物荧光光谱探测绿色植物的可行性。 试验结果表明, 白色、 蓝色和红色三种LED光源照射下SIMCA模型对预测集样本的识别率均达到92%以上, 拒绝率均为100%; 三种光源照射下LDA分类模型均能准确识别出预测集所有样本, 检测效果优于SIMCA模型, 且三种LED光源的效果无显著差异。 为开发低成本绿色植物探测传感器, 建立了绿色植物与非绿色植物样本分类目标函数, 通过粒子群算法(PSO)优选单一连续光谱波段原始光谱并建立了绿色植物和非绿色植物样本的阈值分类模型。 结果表明, 白色、 蓝色和红色LED光源照射下优选的原始光谱波段分别为731.1, 730.76和731.1 nm, 对应阈值分类模型分类预测集样本的F1-score分别为76.71%, 80.52%和78.48%, 蓝色LED光源的效果最好。 该研究优选的主动光源类型和连续检测波段可为开发基于单波段的低成本绿色植物探测传感器提供理论依据。