Design of efficient and effective deep Learning architectures for processing the agricultural images to improve the performance of smart farming

Abstract

Agriculture, a powerful word that requires no definition, contributes the most profitable share of the Indian Economy. On a wider view, intensifying productivity requires very basic things like improving the quality and quantity of the crop yield and dwindling the agricultural expenses. There are certain cases that affect productivity, among which the presence of weeds, crop diseases, and pests in crop fields stands top. Traditional methods, such as manual removal of weeds and spraying of agrochemical products like herbicides, and pesticides have chances of harming the crops and also add to the expenses. Selective treatment against weeds, crop diseases, and pests is a cost-effective method that reduces manpower and usage of the agrochemical, at the same time it requires an effective computer vision system to identify issues and should be smaller in size to run in the resource-constrained device. Machine learning-based activities for identifying weed portions, crop-disease classification, and pest detections are tedious and time-consuming processes. Even though existing deep learning-based approaches perform well, they are hard to deploy on resource-constrained devices commonly used by farmers due to their high computational cost. newlineThis research work presents four contributions, where the first three contributions to handling the challenges of facilitating selective treatment against weeds, crop diseases, and pests, and the fourth contribution is the development of a lightweight computer vision-based Deep Learning based Smart Farming Application. The first contribution presents a method of effective crop weed segmentation in the agricultural field. newline