Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food and bio-system engineering.Artificial Intelligence in Agriculture is an Open Access journal, publishing original research, reviews and perspectives on the theory and practice of artificial intelligence (AI) in agriculture, food and bio-system engineering and related areas. Artificial Intelligence in Agriculture serves as an interdisciplinary forum to share ideas and solutions related to artificial intelligence and applications in agriculture. The journal welcomes both fundamental science and applied research describing the practical applications of AI methods in the fields of agriculture, food - and bio-system engineering and related areas.Topics of interest to the journal include, but are not limited to:AI-based decision support systemsAI-based precision agricultureSmart sensors and Internet of ThingsAgricultural robotics and automation equipmentAgricultural knowledge-based systemsComputational intelligence in agriculture, food and bio-systemsAI in agricultural optimization managementIntelligent interfaces and human-machine interactionMachine vision and image/signal processingMachine learning and pattern recognitionNeural networks, fuzzy systems, neuro-fuzzy systemsSystems modeling and analysisIntelligent systems for animal feedingExpert systems in agricultureCrop Phenotyping and analysisRemote sensing in agricultureAI technology in aquicultureAI in food engineering and cold chain logisticsBig Data and Cloud ComputingAutomatic navigation and self-driving technologyPrecision agricultural aviationDistributed ledger technology (Blockchain)The journal welcomes original research articles, review articles, perspective papers and short communications. The journal's editorial leadership welcome suggestions and proposals for special issues.Editorial Board
Computers and Electronics in Agriculture provides international coverage of advances in the development and application of computer hardware, software, electronic instrumentation, and control systems for solving problems in agriculture, including agronomy, horticulture (in both its food and amenity aspects), forestry, aquaculture, and animal/livestock farming. Its new companion journal, Smart Agricultural Technology provides continuity for smart application being applied in production agriculture.The journal publishes original papers, reviews, and applications notes on topics pertaining to advances in the use of computers or electronics in plant or animal agricultural production, including agricultural soils, water, pests, controlled environments, structures, and wastes, as well as the plants and animals themselves. On-farm, post-harvest operations considered part of agriculture (such as drying, storage, logistics, production assessment, trimming and separation of plant and animal material) are also covered. Relevant areas of technology include artificial intelligence, sensors, machine vision, robotics, networking, and simulation modelling.When determining the suitability of submitted manuscripts for publication, particular emphasis is placed on novelty and innovation, and the degree to which a manuscript advances the state of the art for computers/electronics in agriculture. Applying existing technology to a particular crop for the first time does not qualify as an innovation in computers/electronics for this journal. Research applying off-the-shelf hardware or software, without augmenting such technology with investigator-developed tools, innovations, or unique approaches, should be submitted to its companion journal, Smart Agricultural Technology, whose scope includes applied technology. Manuscripts that apply computers/electronics in an ancillary fashion or focus objectives and conclusions primarily on the application sciences (e.g., entomology, agronomy, engineering, economics, horticulture) should be submitted to one of those respective science journals.The journal recognizes that the use of previously published data sets (either alpha-numeric, quantitative, or imagery) can be extremely beneficial as researchers develop and prototype new machine learning or machine vision algorithms with potential application to agriculture. However, the journal views this prototyping work as preliminary in nature, and prospective authors should, prior to submitting such work to this journal, generate a more scientifically rigorous data set, collected by the authors under controlled and reported experimental conditions.