Precision agriculture

The potential applications of nanomaterials in agriculture for alleviating diverse biotic and abiotic stresses have garnered significant attention. The reported mechanisms encompass promoting plant growth and development, alleviating oxidative stress, inducing defense responses, modulating plant-microbe interactions, and more. However, individual studies may not fully uncover the common pathways or distinguish the effects of different nanostructures. We examined Arabidopsis thaliana transcriptomes exposed to biotic, abiotic, and metal or carbon-based nanomaterials, utilizing 24 microarray chipsets and 17 RNA-seq sets...

Despite advancements in meeting various human needs, energy supply remains a top priority for all countries worldwide. The escalating energy consumption in the agricultural sector underscores the necessity to scrutinize its energy usage. Presently, there exists an absence of a precise tool for accurately measuring this consumption. Hence, this study aims to identify indicators for measuring energy security in agriculture, conducted in three phases: content analysis, indicator validation, and field investigation...

As global food security faces challenges, enhancing crop yield and stress resistance becomes imperative. This study comprehensively explores the impact of nanomaterials (NMs) on Gramineae plants, with a focus on the effects of various types of nanoparticles, such as iron-based, titanium-containing, zinc, and copper nanoparticles, on plant photosynthesis, chlorophyll content, and antioxidant enzyme activity. We found that the effects of nanoparticles largely depend on their chemical properties, particle size, concentration, and the species and developmental stage of the plant...

In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0...

Green fruit detection is of great significance for estimating orchard yield and the allocation of water and fertilizer. However, due to the similar colors of green fruit and the background of images, the complexity of backgrounds and the difficulty in collecting green fruit datasets, there is currently no accurate and convenient green fruit detection method available for small datasets. The YOLO object detection model, a representative of the single-stage detection framework, has the advantages of a flexible structure, fast inference speed and excellent versatility...

Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering-Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0...

Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well-instrumented agricultural site in Nebraska, United States...

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy...

BACKGROUND: Aflatoxin B1 (AFB1 ) and its precursors contaminate food and agricultural products, posing a significant risk to food safety and human health, but simultaneous and effective extraction and determination of AFB1 and its precursors with varied structures is still a challenging task. RESULTS: In this study, a bisimidazolium-type ionic liquid functionalized mesoporous multipod silica (SiO2 @mPMO-IL(im)2 ) was fabricated to extract AFB1 and its two precursors, i...

Bacterial diseases caused substantial yield losses worldwide, with the rise of antibiotic resistance, there is a critical need for alternative antibacterial compounds. Natural products (NPs) from microorganisms have emerged as promising candidates due to their potential as cost-effective and environmentally friendly bactericides. However, the precise mechanisms underlying the antibacterial activity of many NPs, including Guvermectin (GV), remain poorly understood. Here, we sought to explore how GV interacts with Guanosine 5'-monophosphate synthetase (GMPs), an enzyme crucial in bacterial guanine synthesis...

The accurate identification of maize crop row navigation lines is crucial for the navigation of intelligent weeding machinery, yet it faces significant challenges due to lighting variations and complex environments. This study proposes an optimized version of the YOLOX-Tiny single-stage detection network model for accurately identifying maize crop row navigation lines. It incorporates adaptive illumination adjustment and multi-scale prediction to enhance dense target detection. Visual attention mechanisms, including Efficient Channel Attention and Cooperative Attention modules, are introduced to better extract maize features...

Rapeseed ( Brassica napus L. ) has the ability of selenium (Se) enrichment. Identification of selenides in Se-rich rapeseed products will promote the development and utilization of high value. By optimizing the Se species extraction process (protease type, extraction reagent, enzyme sample ratio, extraction time, etc.) and chromatographic column, an efficient, stable, and accurate method was established for the identification of Se species and content in rapeseed seedlings and flowering stalks, which were cultured by inorganic Se hydroponics...

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2...

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, real-world settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context...

INTRODUCTION: Asian soybean rust is a highly aggressive leaf-based disease triggered by the obligate biotrophic fungus Phakopsora pachyrhizi which can cause up to 80% yield loss in soybean. The precise image segmentation of fungus can characterize fungal phenotype transitions during growth and help to discover new medicines and agricultural biocides using large-scale phenotypic screens. METHODS: The improved Mask R-CNN method is proposed to accomplish the segmentation of densely distributed, overlapping and intersecting microimages...

With aggravated abiotic and biotic stresses from increasing climate change, metal-organic frameworks (MOFs) have emerged as versatile toolboxes for developing environmentally friendly agrotechnologies aligned with agricultural practices and safety. Herein, we have explored MOF-based agrotechnologies, focusing on their intrinsic properties, such as structural and catalytic characteristics. Briefly, MOFs possess a sponge-like porous structure that can be easily stimulated by the external environment, facilitating the controlled release of agrochemicals, thus enabling precise delivery of agrochemicals...

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture...

One of the most concerning global environmental issues is the pollution of agricultural soils by heavy metals (HMs), especially cadmium, which not only affects human health through Cd-containing foods but also impacts the quality of rice. The soil's nitrification and denitrification processes, coupled with the release of volatile organic compounds by plants, raise substantial concerns. In this review, we summarize the recent literature related to the deleterious effects of Cd on both soil processes related to the N cycle and rice quality, particularly aroma, in different water management practices...

Advances in bioinspired and biohybrid robotics are enabling the creation of multifunctional systems able to explore complex unstructured environments. Inspired by Avena fruits, we report a biohybrid miniaturized autonomous machine (HybriBot) composed of a biomimetic biodegradable capsule as cargo delivery system and natural humidity-driven sister awns as biological motors. We employ micro-computed tomography, molding via two-photon polymerization and casting of natural awns into biodegradable materials to fabricate multiple HybriBots capable of exploring various soil and navigating soil irregularities, such as holes and cracks...

Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes...