In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

Precision Agriculture for Crop and Livestock Farming-Brief Review.

Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept

Palm oil is a renewable resource that has the potential to replace fossil fuel and petrochemical for a better sustainable system. However, there is room for improvement in the current operation of the palm oil industry to achieve better sustainability development. The industrial revolution toward automation and artificial intelligence (AI) is the new trend known as the fourth industrial revolution (Industry 4.0). Unfortunately, the palm oil industry has been moving slowly in this revolution. This paper aims to conduct a detailed review of the current state of the palm oil industry development toward Industry 4.0. A novel Hazard and Operability Analysis (HAZOP) approach is adopted i) to ensure a detailed evaluation of the existing problems, and ii) to identify potential implementation of Industry 4.0 technologies in the palm oil industry. HAZOP is a common approach used in chemical engineering to systematically evaluate process safety and identify the possible improvement of the existing system. The same concept is applied in this paper to investigate the possible adaptation of Industry 4.0 technologies to improve the palm oil industry. Existing Industry 4.0 technologies and features were evaluated to identify feasible adaptation in the industry. The HAZOP review proposed 23 recommendations to improve the palm oil industry with Industry 4.0 technologies to achieve a higher standard in sustainable production. A total of 13 specific Industry 4.0 features were identified as the potential development gaps for palm oil industry stakeholders, which included the adaptation of Internet-of-Things sensors, cloud computing, blockchain, and smart imaging processing technologies.

A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach

Suitable fertilization depth can improve the water productivity and maize yield by regulating development of the root system

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. 

Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management

Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency

Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation

Background and aims Water-saving ground cover rice production systems (GCRPS) are gaining popularity in many parts of the world. We aimed to describe the characteristics of root growth, morphology, distribution, and water uptake for a GCRPS. Methods A traditional paddy rice production system (TPRPS) was compared with GCRPS in greenhouse and field experiments. In the greenhouse, GCRPS where root zone average soil water content was kept near saturation (GCRPSsat), field capacity (GCRPSfwc) and 80% field capacity (GCRPS80%), were evaluated. In a two-year field experiment, GCRPSsat and GCRPS80% were applied. Results Similar results were found in greenhouse and field experiments. Before mid-tillering the upper soil temperature was higher for GCRPS, leading to enhanced root dry weight, length, surface area, specific root length, and smaller diameter of roots but lower water uptake rate per root length compared to TPRPS. In subsequent growth stages, the reduced soil water content under GCRPS caused that the preponderance of root growth under GCRPSsat disappeared in comparison to TPRPS. Under other GCRPS treatments (GCRPSfwc and GCRPS80%), significant limitation on root growth, bigger root diameter and higher water uptake rate per root length were found. Conclusions Discrepancies in soil water and temperature between TPRPS and GCRPS caused adjustments to root growth, morphology, distribution and function. Even though drought stress was inevitable after mid-tillering under GCRPS, especially GCRPS80%, similar or even enhanced root water uptake capacity in comparison to TPRPS might promote allocation of photosynthetic products to shoots and increase water productivity.

/pdf/characterizing-roots-and-water-uptake-in-a-ground-cover-rice-27m953gar2.pdf

Characterizing roots and water uptake in a ground cover rice production system.

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Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency

Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation

Characterizing roots and water uptake in a ground cover rice production system.