This paper reviews artificial intelligent noses (or electronic noses) as a fast and noninvasive approach for the diagnosis of insects and diseases that attack vegetables and fruit trees. The particular focus is on bacterial, fungal, and viral infections, and insect damage. Volatile organic compounds (VOCs) emitted from plants, which provide functional information about the plant’s growth, defense, and health status, allow for the possibility of using noninvasive detection to monitor plants status. Electronic noses are comprised of a sensor array, signal conditioning circuit, and pattern recognition algorithms. Compared with traditional gas chromatography–mass spectrometry (GC-MS) techniques, electronic noses are noninvasive and can be a rapid, cost-effective option for several applications. However, using electronic noses for plant pest diagnosis is still in its early stages, and there are challenges regarding sensor performance, sampling and detection in open areas, and scaling up measurements. This review paper introduces each element of electronic nose systems, especially commonly used sensors and pattern recognition methods, along with their advantages and limitations. It includes a comprehensive comparison and summary of applications, possible challenges, and potential improvements of electronic nose systems for different plant pest diagnoses.

Arugula (Eruca sativa) is cultivated using hydroponic techniques in greenhouses to fulfill high year-round demand, but its nutrient management in hydroponic production has not yet been standardized, potentially leading to limited quality and productivity. Aiming to address this issue, we investigated the effect of electrical conductivity (EC) on yield, nutritional and phytochemical properties of arugula. The model cultivar arugula ‘Standard’ was grown at four different EC levels (1.2, 1.5, 1.8, and 2.1 dS·m⁻¹). Our results indicated photosynthetic properties, SPAD, leaf area, yield and dry weight increased with increasing EC from 1.2 to 1.8 dS·m⁻¹. Foliar nutrient content increased with higher EC, but nutrient solution with 2.1 dS·m⁻¹ showed a significant decline in N, Ca and most of the micronutrients including Fe, Zn, Mo, Cu, B and Mn. Total glucosinolates, total chlorophyll and total carotenoids concentrations increased with increasing EC. In addition, total anthocyanin content was highest in plants grown in EC 1.2 and 2.1 dS·m⁻¹, demonstrating a stress response when grown in extreme EC levels. Our results further indicated a rapid accumulation of nitrate with higher EC, potentially detrimental to human health. This research demonstrated the optimal EC range would be 1.5 to 1.8 dS·m⁻¹ for arugula in hydroponic production systems based on yield, quality criteria and human health considerations.

An electronic nose (E-nose) system equipped with a sensitive sensor array was developed for fast diagnosis of aphid infestation on greenhouse tomato plants at early stages. Volatile organic compounds (VOCs) emitted by tomato plants with and without aphid attacks were detected using both the developed E-nose system and gas chromatography mass spectrometry (GC-MS), respectively. Sensor performance, with fast sensor responses and high sensitivity, were observed using the E-nose system. A principle component analysis (PCA) indicated accurate diagnosis of aphid-stressed plants compared to healthy ones, with the first two PCs accounting for 86.7% of the classification. The changes in VOCs profiles of the healthy and infested tomato plants were quantitatively determined by GC-MS. Results indicated that a group of new VOCs biomarkers (linalool, carveol, and nonane (2,2,4,4,6,8,8-heptamethyl-)) played a role in providing information on the infestation on the tomato plants. More importantly, the variation in the concentration of sesquiterpene VOCs (e.g., caryophyllene) and new terpene alcohol compounds was closely associated with the sensor responses during E-nose testing, which verified the reliability and accuracy of the developed E-nose system. Tomato plants growing in spring had similar VOCs profiles as those of winter plants, except several terpenes released from spring plants that had a slightly higher intensity.

An electronic nose (E-nose) system equipped with a gas sensor array and real-time control panel was developed for a fast diagnosis of whitefly infestation in tomato plants. Profile changes of volatile organic compounds (VOCs) released from tomato plants under different treatments (i.e., whitefly infestation, mechanical damage, and no treatment) were successfully determined by the developed E-nose system. A rapid sensor response with high sensitivity towards whitefly-infested tomato plants was observed in the E-nose system. Results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) indicated that the E-nose system was able to provide accurate distinguishment between whitefly-infested plants and healthy plants, with the first three principal components (PCs) accounting for 87.4% of the classification. To reveal the mechanism of whitefly infestation in tomato plants, VOC profiles of whitefly-infested plants and mechanically damaged plants were investigated by using the E-nose system and GC-MS. VOCs of 2-nonanol, oxime-, methoxy-phenyl, and n-hexadecanoic acid were only detected in whitefly-infested plants, while compounds of dodecane and 4,6-dimethyl were only found in mechanically damaged plant samples. Those unique VOC profiles of different tomato plant groups could be considered as bio-markers for diagnosing different damages. Moreover, the E-nose system was demonstrated to have the capability to differentiate whitefly-infested plants and mechanically damaged plants. The relationship between sensor performance and VOC profiles confirmed that the developed E-nose system could be used as a fast and smart device to detect whitefly infestation in greenhouse cultivation.

Kale (Brassica napus) and collard (Brassica oleracea) are two leafy greens in the family Brassicaceae. The leaves are rich sources of numerous health-beneficial compounds and are commonly used either fresh or cooked. This study aimed to optimize the nutrient management of kale and collard in hydroponic production for greater yield and crop quality. ‘Red Russian’ kale and ‘Flash F1’ collard were grown for 4 weeks after transplanting in a double polyethylene-plastic-covered greenhouse using a nutrient film technique (NFT) system with 18 channels. Kale and collard were alternately grown in each channel at four different electrical conductivity (EC) levels (1.2, 1.5, 1.8, and 2.1 mS·cm⁻¹). Fresh and dry yields of kale increased linearly with increasing EC levels, while those of collard did not increase when EC was higher than 1.8 mS·cm⁻¹. Kale leaves had significantly higher P, K, Mn, Zn, Cu, and B than the collard at all EC levels. Additionally, mineral nutrients (except N and Zn) in leaf tissue were highest at EC 1.5 and EC 1.8 in both the kale and collard. However, the changing trend of the total N and NO3- of the leaves showed a linear trend; these levels were highest under EC 2.1, followed by EC 1.8 and EC 1.5. EC levels also affected phytochemical accumulation in leaf tissue. In general, the kale leaves had significantly higher total anthocyanin, vitamin C, phenolic compounds, and glucosinolates but lower total chlorophylls and carotenoids than the collard. In addition, although EC levels affected neither the total chlorophyll or carotenoid content in kale nor glucosinolate content in either kale or collard, other important health-beneficial compounds (especially vitamin C, anthocyanin, and phenolic compounds) in kale and collard leaves reduced with the increasing EC levels. In conclusion, the kale leaf had more nutritional and phytochemical compounds than the collard. An EC level of 1.8 mS·cm⁻¹ was the optimum EC level for the collard, while the kale yielded more at 2.1 mS·cm⁻¹. Further investigations are needed to optimize nitrogen nutrition for hydroponically grown kale.

In greenhouse energy balance models, the soil thermal parameters are important for evaluating the heat transfer between the greenhouse air and the soil. In this study, the soil thermal diffusivity was estimated from greenhouse soil temperature data using the amplitude, phase-shift, arctangent, logarithmic, and min-max methods. The results showed that the amplitude method and the min-max method performed well in estimating the soil thermal diffusivity. The obtained soil thermal diffusivity was input into a sinusoidal model to determine the greenhouse soil temperature at different soil depths. For greenhouse applications, the daily average soil temperature at different depths was predicted according to the temperature at the surface and the annual mean soil temperature. The model was validated using soil temperature data from summer and winter, when the greenhouse was cooled and heated, respectively.