High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.

Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root’s structural information. To measure the root damages caused by WCR non-destructively, this study utilized MISIRoot, a minimally invasive and in situ automatic plant root phenotyping robot to collect not only high-resolution images but also 3D positions of the roots without uprooting. To identify roots in the images and to study how the damages were distributed in different types of roots, a deep convolution neural network model was trained to differentiate the relatively thick and thin roots. In addition, a color camera was used to capture the above-ground morphological features, such as the leaf color, plant height, and side-view leaf area. To check if the plant shoot had any visible symptoms in the inoculated group compared to the control group, several vegetation indices were calculated based on the RGB color. Additionally, the shoot morphological features were fed into a PLS-DA model to differentiate the two groups. Results showed that none of the above-ground features or models output a statistically significant difference between the two groups at the 95% confidence level. On the contrary, many of the root structural features measured using MISIRoot could successfully differentiate the two groups with the smallest t-test p-value of 1.5791 × 10⁻⁶. The promising outcomes were solid proof of the effectiveness of MISIRoot as a potential solution for identifying WCR infestations before the plant shoot showed significant symptoms.

As one of the largest supplied grain crops, corn plants often require a significant amount of nitrogen fertilizer for optimal yield. However, excessive fertilizer usage can lead to adverse environmental consequences, especially for the nearby hydrological network. To precisely manage nitrogen application, accurate measurement of corn crop nitrogen deficiency is necessary. Hyperspectral imaging (HSI) techniques are widely applied in plant phenotyping to effectively measure plant traits caused by biotic or abiotic stresses. While previous HSI processing methods primarily focus on the overall color change, they rarely analyze the signal from the leaf-level spatial domain. However, early-stage nitrogen deficiency symptoms may not significantly alter the overall color, resulting in limited model performance in such cases. A newly developed HSI device called LeafSpec can scan an entire corn leaf with a high signal-over-noise ratio paired with high spatial-spectral resolution, capturing the detailed color changes at the leaf structure level. This study focused on identifying distinctive nitrogen deficiency indicators using an innovative methodology that applies spectral analysis to the details of leaf venation structures. The study started with developing an automated venation segmentation algorithm to separate a whole corn leaf into structural components. An in-depth examination of the spectral profiles associated with different leaf components introduced a new spatial-spectral index, demonstrating a higher correlation with the nitrogen content data than the averaged spectral indices. The high-resolution spectral-structural features discovered with this method provided new potential to improve the performance of the nitrogen prediction model in terms of both accuracy and robustness.

Hyperspectral imaging (HSI) is being widely applied in plant phenotyping platforms. Some new HSI devices such as LeafSpec was introduced recently which can provide a high signal-over-noise ratio along with higher spectral and spatial resolutions. However, most of the previous image processing algorithms only calculated the averaged spectrum from the leaf, but rarely include the spatially distributed information on the leaf level. Meanwhile, different nutrient stresses could result in different color patterns on the leaf which can be used to furtherly improve the quality of plant phenotyping. This study focused on the development of a new methodology that applies spatial distribution analysis on HSI soybean leaf images. Firstly, a novel way of encoding all the leaf pixels to a new coordinate system called Natural Leaf Coordinate System (NLCS) was developed. NLCS defined the coordinates of every pixel relative to the leaf venation so that the following spatial distribution analysis could be conducted more intuitively. Second, a new nitrogen index based on NLCS called NLCS-N was developed and able to outperform the whole leaf averaged NDVI by having a better correlation with the plants' nitrogen contents, and a more significant differentiation between the nitrogen-sufficient versus the nitrogen-deficient plants.

Accurate body-weight monitoring is essential for assessing welfare in cage-free poultry. However, commercial farms continue to rely on manual weighing because of concerns regarding the accuracy and reliability of automated methods. This study developed and evaluated an Internet of things (IoT)-enabled weighing platform integrating load cells, an microcontroller, a Raspberry Pi 5, and Node-RED for data acquisition, processing, and visualization. The system recorded weight measurements at 1 Hz, detected individual weighing sessions, and applied a rolling-median filter to produce stable weight estimates. Validation was performed against a reference scale during two weighing sessions one week apart using 75 cage-free hens randomly selected from a flock of 750 Hy-Line W80 birds. Bland–Altman analysis and a linear mixed-effects model indicated a small overestimation of approximately 6–9 g, with most measurements falling within the 95% limits of agreement, while overall mean absolute percentage error remained below 3%. Improved accuracy during the second session suggests that platform stability influenced performance. Overall, the system demonstrates strong potential for continuous low-stress weight monitoring in poultry farms. Future improvements should focus on refining calibration methods, enhancing mechanical stability, and integrating bird identification and presence-detection mechanisms to further support flock management and welfare monitoring.