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.