Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Smart Farming Using Robots in IoT to Increase Agriculture Yields: A Systematic Literature Review

Robots are beneficial in everyday life, especially in helping food security in the agricultural industry. Smart farming alone is not enough because smart farming is only automated without mobile hardware. The existence of robots can minimize human involvement in agriculture so that humans can maximize activities outside of farms. This Study aims to review articles regarding robots in smart farming to increase agriclture yields. This article systematically uses the systematic literature review method utilizing the Preferred reporting items for systematic review and meta-analyses (PRISMA) by submitting 3 Research Questions (RQ). According to the authors of the 3 RQs, it is necessary to represent the function and purpose of robots in farms and to be used in the context of the importance of robots in agriculture because of the potential impact of increase agriculture yields. This Research contributes to finding and answering 3 RQ, which are the roots of the use of robots. The results taken, the authors get 116 articles that can be reviewed and answered RQ and achieve goals. RQ 1 was responded to with the article's country of origin, research criteria, and the year of the article. In RQ 2 the author answered that Research often carried out 6 schemes, then the most Research was (Challenge Robots, Ethics, and Opinions in Agriculture) and (Design, Planning, and Robotic Systems in Agriculture). Finally, in RQ 3, the author describes the research scheme based on understanding related Research. The author hopes this basic scheme can be a benchmark or a new direction for future researchers and related agricultural industries to improve agricultural quality

Robotic Picking of Tangle-prone Materials (with Applications to Agriculture).

The picking of one or more objects from an unsorted pile continues to be non-trivial for robotic systems. This is especially so when the pile consists of individual items that tangle with one another, causing more to be picked out than desired. One of the key features of such tangling-prone materials (e.g., herbs, salads) is the presence of protrusions (e.g., leaves) extending out from the main body of items in the pile.This thesis explores the issue of picking excess mass due to entanglement such as occurs in bins composed of tangling-prone materials (TPs), especially in the context of a one-shot mass-constrained robotic bin-picking task. Specifically, it proposes a human-inspired entanglement reduction method for making the picking of TPs more predictable. The primary approach is to directly counter entanglement through pile interaction with an aim of reducing it to a level where the picked mass is predictable, instead of avoiding entanglement by picking from collision or entanglement-free points or regions. Taking this perspective, several contributions are presented that (i) improve the understanding of the phenomenon of entanglement and (ii) reduce the picking error (PE) by effectively countering entanglement in a TP pile.First, it studies the mechanics of a variety of TPs improving the understanding of the phenomenon of entanglement as observed in TP bins. It reports experiments with a real robot in which picking TPs with different protrusion lengths (PLs) results in up to a 76% increase in picked mass variance, suggesting PL be an informative feature in the design of picking strategies. Moreover, to counter the inherent entanglement in a TP pile, it proposes a new Spread-and-Pick (SnP) approach that significantly reduces entanglement, making picking more consistent. Compared to prior approaches that seek to pick from a tangle-free point in the pile, the proposed method results in a decrease in PE of up to 51% and shows good generalisation to previously unseen TPs

Herbicides

Weeds severely affect crop quality and yield. Therefore, successful farming relies on their control by coordinated management approaches. Among these, chemical herbicides are of key importance. Their development and commercialization began in the 1940's and they allowed for a qualitative increase in crop yield and quality when it was most needed. This book blends review chapters with scientific studies, creating an overview of some the current trends in the field of herbicides. Included are environmental studies on their toxicity and impact on natural populations, methods to reduce herbicide inputs and therefore overall non-target toxicity, and the use of bioherbicides as natural alternatives

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Agricultural Diversification

Agricultural diversification can occur in many forms (e.g., genetic variety, species, structural) and can be created temporally and over different spatially scales (e.g., within crop, within field, and landscape level). Crop diversification is the practice of growing more than one crop species within a farming area in the form of rotations (two or more crops on the same field in different years), multiple crops (more than one crop in the same season on the same field) or intercropping (at least two crops simultaneously on the same field).Various cropping strategies and management practices, such as diversification of cropping systems by crop rotation, conservation tillage, and the use of cover crops, have been promoted to enhance crop productivity and ecosystem services. However, the opportunities and means differ among regions and the actual effects of diversification on cropping system sustainability still need more investigation.This Special Issue covers the state-of-the-art and recent progress in different aspects related to agricultural diversification to increase the sustainability and resilience of a wide range of cropping systems (grassland, horticultural crops, fruit trees) and in a scenario of environmental challenges due to climate change: Crop production and quality; Impact of crop diversification on soil quality and biodiversity; Environmental impact and delivery of ecosystem services by crop diversification

Digitally-enabled crop disorder management based on farmer empowerment for improved outcomes

Crop disorder incidents such as pests and disease attacks are the major reason of crop losses that require timely actions and can adversely affect agriculture production. In order to address this problem, a model was designed that empowers farmers to identify crop disorder incidents instantly and manage them effectively by providing relevant information in context. In contrast, the existing approaches reported in the literature rely on identifying crop disorders from images that depict the presence of symptoms in the crop. However, due to the inherent characteristics of the images, these approaches are effective only in more controlled environments and provide limited support in crop disorder identification. We have created a crop disorder search space model that is composed of mapping between different crop disorders and symptom(s) that provide unique identification characteristics specific to each crop disorder. We call these unique mappings as disorder identifiers. This model was later converted into a mobile-based artifact, and the information required to perform the search operation on the search space was obtained from farmers through it. The artifact was deployed among a group of farmers to evaluate how well it could aid in identifying crop disorders. It was noted that the developed artifact was able to identify most crop disorders instantly, mitigating the issues associated with crop disorder identification. In the rest of the cases, it gives subject experts the ability to identify crop disorders. The experiments conducted on the effectiveness and usability of the artifact indicate that disorder identifiers providing clear and consistent representations of the presence of crop disorders can be used to identify them rapidly. Further, it has been also demonstrated that farmers are capable of correlating their field observations with a list of crop disorder identifiers provided through the artifact. The correct selection of the disorder identifier will lead farmers to know about the presence of crop disorder in the field and recommend control measures instantly. Moreover, farmers’ perception of various impact indicators showed that, as compared to previous cultivation seasons, yield quality and quantity losses were reduced due to the reduced crop disorder incidents. The application of agrochemicals and associated expenses of farmers were also significantly reduced, thereby increasing their revenues