Agriculture Software in Modern Farming Solutions
Artificial Intelligence (AI) was recently introduced in agriculture to improve harvest quality and accuracy in farming along with bringing automation to solve some problems in agriculture like labor, climate change, changing market demands, and so forth. Data-based early-warning systems are developed to measure temperature, humidity, diseases in plants, pests, and poor plant nutrition and inform farmers to take required immediate actions. The incorporation of information and communication technologies into machinery, equipment, and sensors is important for data transmission and the concentration of data in remote storage systems for decision making. AI in agriculture is also used to make seasonal forecasting models for agriculture accuracy and productivity. It is specifically valuable for small farms as they have limited data and knowledge, but produce 70% of the world’s crops.
AI in agriculture is evolving steadily and bringing a revolution in agriculture. It falls into three major categories. Agriculture robots handling agriculture tasks like harvesting crops at a faster pace compared to human labor. Crop and soil monitoring with computer vision and deep-learning algorithms to process captured data for monitoring the health of soil and crop. Predictive analytics using machine learning (ML) models to track and predict different environmental impacts on crop yield.
Smart farming has computing elements embedded in objects and interconnected with each other and the internet. It was originated with software engineering and computer science and the ideas of the farm management information system came up. Agriculture software to collect, process, store, and disseminate a large amount of data sensed in the field into the required format. A research conducted in Europe revealed the common functions of agriculture software that are field operations management (63%), reporting (57%), finance (45%), and site-specific management (40%). Data management needs advanced technologies depending on the development of agriculture software to analyze and process the data collected.
Agriculture software embedded in AI systems optimize the resources required irrigation, weeding, and spraying and thus saves the excess use of water, pesticides, herbicides by maintaining the soil fertility. Agriculture solutions rich with AI technology and software platforms to form better communication channels, and effective processing of the data is in demand and thus research and development of various machines are in progress. The integration of various available systems is difficult and was identified as one of the main limiting factors to develop farm management solutions. Other limiting factors include the education, ability, and skills of farmers to understand and handle modern agriculture solutions coming to the market.
