LiDAR-Based Negative Obstacle Detection for Unmanned Ground Vehicles in Orchards.

In orchard environments, negative obstacles such as ditches and potholes pose significant safety risks to robots working within them. This paper proposes a negative obstacle detection method based on LiDAR tilt mounting. With the LiDAR tilted at 40°, the blind spot is reduced from 3 m to 0.

Research on Inter-Frame Feature Mismatch Removal Method of VSLAM in Dynamic Scenes.

Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot's pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impacting the accuracy of the mobile robot's self-localization and mapping. This paper proposes a method for removing mismatches of image features in dynamic scenes in visual SLAM.

Uptake, accumulation and toxicity of short chain chlorinated paraffins to wheat (Triticum aestivum L.).

Short chain chlorinated paraffins (SCCPs) are ubiquitous persistent organic pollutants. They have been widely detected in plant-based foods and might cause adverse impacts on humans. Nevertheless, uptake and accumulation mechanisms of SCCPs in plants remain unclear.

Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning.

Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possibility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off-flavor-related compounds.

Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast.

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique.

Microalgae Bioactive Carbohydrates as a Novel Sustainable and Eco-Friendly Source of Prebiotics: Emerging Health Functionality and Recent Technologies for Extraction and Detection.

There is a global interest in the novel consumption, nutritional trends, and the market of new prebiotic sources and their potential functional impacts. Commercially available nutritional supplements based on microalgae that are approved to be edible by FDA, like (Cyanobacteria) and (Chlorophyta) become widely attractive. Microalgae are rich in carbohydrates, proteins, and polyunsaturated fatty acids that have high bioactivity.

Rapid and nondestructive detection of marine fishmeal adulteration by hyperspectral imaging and machine learning.

Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP).

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum.

Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessment of plant food for safe consumption. Thus, a confocal Raman spectroscopy combined with density functional theory and deep learning was used for characterizing phytochelatin2 (PC), and Cd-PC mixtures.

Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information.

Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC), Cd-PC mixture standards, and pak choi (Brassica chinensis) leaves as a plant model.

Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing.

Background: Fractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC with medium to high spatial resolution on the ground. However, PDM-based FVC estimation is limited by effects stemming from the variation of crop canopy chlorophyll content (CCC).

A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages.

Background: Timely and accurate estimates of canopy chlorophyll (Chl) a and b content are crucial for crop growth monitoring and agricultural management. Crop canopy reflectance depends on many factors, which can be divided into the following categories: (i) leaf effects (e.g.

A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique.

The automated harvesting of strawberry brings benefits such as reduced labor costs, sustainability, increased productivity, less waste, and improved use of natural resources. The accurate detection of strawberries in a greenhouse can be used to assist in the effective recognition and location of strawberries for the process of strawberry collection. Furthermore, being able to detect and characterize strawberries based on field images is an essential component in the breeding pipeline for the selection of high-yield varieties.

A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks.

Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head.

Comparative de novo transcriptomics and untargeted metabolomic analyses elucidate complicated mechanisms regulating celery (Apium graveolens L.) responses to selenium stimuli.

Presently, concern regarding the effects of selenium (Se) on the environment and organisms worldwide is increasing. Too much Se in the soil is harmful to plants. In this study, Illumina RNA sequencing and the untargeted metabolome of control and Se-treated celery seedlings were analyzed.

Comparative proteomic analysis of pepper ( L.) seedlings under selenium stress.

Selenium (Se) is an essential trace element for human and animal health. Se fertilizer has been used to increase the Se content in crops to meet the Se requirements in humans and animals. To address the challenge of Se poisoning in plants, the mechanisms underlying Se-induced stress in plants must be understood.

Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.

The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.

Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform.

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements.

Comparative proteomic analysis of okra (Abelmoschus esculentus L.) seedlings under salt stress.

Background: Salinization seriously threatens land use efficiency and crop yields across the world. Understanding the mechanisms plants use to protect against salt stress will help breeders develop salt-tolerant vegetable crops. Okra (Abelmoschus esculentus L.

Monitoring zearalenone in corn flour utilizing novel self-enhanced electrochemiluminescence aptasensor based on NGQDs-NH-Ru@SiO luminophore.

Accurate and early diagnosis of mycotoxin is particularly significant to the food and agricultural product safety. In the present work, a sensitive and effective monitoring method for zearalenone (ZEN) was exploited based on a novel self-enhanced electrochemiluminescence (ECL) aptasensor. The self-enhanced lumonophore was compounded by electrostatically combining amine-functionalized Ru(bpy)-doped silica nanoparticles (NH-Ru@SiO NPs) and nitrogen doped graphene quantum dots (NGQDs) together.

The Phosphoproteomic Response of Okra ( L.) Seedlings to Salt Stress.

Soil salinization is a major environmental stresses that seriously threatens land use efficiency and crop yields worldwide. Although the overall response of plants to NaCl has been well studied, the contribution of protein phosphorylation to the detoxification and tolerance of NaCl in okra ( L.) seedlings is unclear.