Crop cultivation is a tedious and laborious process. One of the challenges faced by farmer is the lack of knowledge and manpower to identify diseased crops at an early stage to prevent the spread of infections to healthy crops. The traditional method of checking for diseases in plants is through visual checks, but this method is not consistent, and is inefficient in detecting the diseases associated with plants. RP has developed an efficient artificial intelligence (AI)-based computer vision system that can detect plant diseases.
It uses computer vision and video analytics to evaluate the probability of each disease for plants of any color. There are many variations of the same disease among each class of crops; every disease presents specific characteristics that make them different from others, e.g., colour, texture, shape. These characteristics and their various combinations are used by the algorithm to determine the specific disease present. The trained model detects diseased plants within acceptable confidence limit.
Once co-related with real-time data, it can be a predictive tool to enable farmers to “predict” the probability of the plant disease with a known accuracy. Further developments with appropriate refinements to the type, suitability, sensitivities, precision and accuracy of the detector may enable early detection of other diseases.
Reliable and productive compared to visual checks.
Allow early intervention to prevent the spread of plant disease, and hence improve overall yield.
Reduce manpower and labour required to do visual checks.
The proof-of-concept has been completed, and this technology is looking for project collaborators or consultancy projects to test the technology on the client’s crop choice.
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