Computer vision-based automation has become popular in detecting and monitoring plants'
nutrient deficiencies in recent times. The predictive model developed by various researchers …

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis
and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery …

Paddy is the staple food for more than 50% of 138 billion Indian population. Inorder to meet
with the growing demand, farmers often resort to application of synthetic fertilizers and plant …

Edge-assisted Internet of Agriculture Things (Edge-IoAT) connects massive smart devices
managed by edge nodes to collect crop data for distributed computing, such as federated …

Conventional methods of data sampling in agriculture are time consuming, labor intensive,
destructive, subject to human error and affected by field conditions. Thus, remote sensing …

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield
of paddy. Therefore, the paper proposes a classification system for the identification of pest …

Proso millet (Panicum miliaceum L.), one of the major cultivated millets, serves as a
complement to major cereal crops due to its drought tolerance and low input demands …

The Nutrition of a crop is very essential for the health conditions during its growth stages and
yield. A plant development is dependent on various nutrients absorbed from the natural …

In recent years, identifying plant nutrient deficiency is considered a critical issue in smart
agriculture research. Identifying plant nutrient deficiency at an early stage will undoubtedly …

It is difficult to obtain real-time crop water shortage during spinach seedling production, so
this study proposes a real-time spinach seedling water stress classification method based …