Chilli Leaf Diseases Detection with Different Features of Original Chilli Using Region Based Convolutional Neural Network - K. , Raja; Duela J. , Shiny; Gopichandd , M.; Kannan , Karthik; Sathish , M. G. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The productivity of the agriculture sector drives the Indian economy. Rural households rely on agriculture to a greater than 70% extent. Almost 60% of the country is employed in agriculture, which generates roughly 17% of the global GDP. Thus, within the domain of agriculture, the detection of crop diseases is crucial. Rice, wheat, groundnuts, and including variety of crops, but not limited to, fruits, vegetables, and other plants. In addition to these crops, Indian farmers also raise potatoes, oilseeds, sugarcanes, and non-food commodities including coffee, cocoa, tea, rubber, and cotton. Most plants primarily grow depending on the energy of their roots and leaves. There are also some more factors that result in various plant-leaf diseases, which ruin harvests and ultimately impact the nation’s economy. Chilli production is a skilled labor-intensive operation because plants are constantly under attack from various insects, bacterial diseases, and smaller-scale organisms. Studies of organic products show that leaves and shoots are commonly used to identify attack marks. Currently, chemicals are being applied to plants without paying attention to their needs. This technique will ensure that chemicals such as pesticides are only applied to plants when they are infected with the diseases. Images of the chilli leaf disease were captured using image processing techniques. The leaf image will be used to identify and estimate the state of the plants. There are also several data processing techniques, which are indeed effective as well as efficient for determining plant diseases to support farmers. In this, we have implemented GLCM feature extraction and region-based CNN method. Our Proposed model aims to give an accuracy of above 90%. It will be helpful in various agricultural applications. Firstly, it can be beneficial for assisting non-expert farmers in identifying the right time to apply chemicals to the plant. It also provides the most appropriate time for harvesting crops before they get ruined. Secondly, it can also be used by the researchers of plant research institutes to study crops in detail in different stages of the crop.