Research on Artificial Intelligence based Fruit Disease Identification System (AI-FDIS) with the Internet of Things (IoT)

Monitoring fruit quality, volume, and development on the plantation are critical to ensuring that the fruits are harvested at the optimal time. Fruits are more susceptible to the disease while they are actively growing. It is possible to safeguard and enhance agricultural productivity by early detection of fruit diseases. A huge farm makes it tough to inspect each tree to learn about its fruit personally. There are several applications for image processing with the Internet of Things (IoT) in various fields. To safeguard the fruit trees from illness and weather conditions, it is difficult for the farmers and their workers to regularly examine these large areas. With the advent of Precision Farming, a new way of thinking about agriculture has emerged, incorporating cutting-edge technological innovations. One of the modern farmers’ biggest challenges is detecting fruit diseases in their early stages. If infections aren’t identified in time, farmers might see a drop in income. Hence this paper is about an Artificial Intelligence Based Fruit Disease Identification System (AI-FDIS) with a drone system featuring a high-accuracy camera, substantial computing capability, and connectivity for precision farming. As a result, it is possible to monitor large agricultural areas precisely, identify diseased plants, and decide on the chemical to spray and the precise dosage to use. It is connected to a cloud server that receives images and generates information from these images, including crop production projections. The farm base can interface with the system with a user-friendly Human-Robot Interface (HRI). It is possible to handle a vast area of farmland daily using this method. The agricultural drone is used to reduce environmental impact and boost crop productivity.

Influence of fermented tannery solid waste on morphological, biochemical, yield and nutritional responses of tomato plants

The non-tanned proteinaceous tannery solid waste animal fleshing (ANFL), containing high nutritive value, was hydrolyzed using bacteria Selenomonas ruminantium HM000123 through submerged (SmF) and solid-state (SSF) fermentation processes. In addition, the effects of ANFL fermentative hydrolysate on growth, yield and biochemical properties of tomato plants were investigated. The treatments included T1 (SmF-ANFL), T2 (SSF-ANFL), T3 (recommended dose of NPK fertilizers) and a control without any amendment. Hydrolysates of both SmF-ANFL and SSF-ANFL treatments increased the biomass and yield as evidenced by plant height, stem girth, number of leaves and fruit yield when compared with both NPK and control plants. In this 90-day study, significant (p ≤ 0.05) changes were observed in SSF-ANFL treated plants compared to the other treatments. Protein profile analyzed through SDS-PAGE indicates the expression of a high molecular weight protein (205 kDa) and other proteins in the leaves of the SSF-ANFL treated plants. Overall results revealed that SSF-ANFL can be successfully utilized as a fertilizer particularly for cultivating tomato plants.