Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

Fiber and vitamin C rich food plays an important role for human health. The aim of this study was to prepare the fiber and vitamin C rich biscuits and to quantify the levels of fiber, protein, fat, and vitamin C in prepared amla pomace biscuits. The fiber content of biscuits ranges from 1.7 – 5.2% and the maximum fiber was obtained for biscuits prepared from 15% amla pomace. The fat content of biscuits varies from 14.03 – 18.03% and the manimum fiber was obtained for biscuits prepared from 15% amla pomace. The prepared biscuits are rich in Vitamin C which increases with increase in pomace concentration and varies from 26.28 – 49.15 mg/ 100 gm. The ash content of biscuits ranges decreases with increased concentration of pomace and protein content varies from 7.27 – 7.52%.

The development of highly nutritious bakery products with optimum utilization of food waste is a major challenge for the food industry. The optimum utilization of food waste for the sustainable development goal of the country is important for the growth of the nation. The aim of the present work is to prepare value-added composite flour-mixed bread from waste fruit and vegetables. The composite flour was prepared in four formulations of peel and pomace with wheat flour (PPWF), as PPWF1, PPWF2, PPWF3, and PPWF4. Composite flour was blended with a mix of vegetable and fruit pomace powders and whole wheat flour. Indian gooseberry pomace powder, apple pomace powder, bottle gourd peel powder, and potato peel powder were used with whole wheat flour to make pomace and whole wheat flour compositions such as PPWF1, PPWF2, PPWF3, and PPWF4. Out of these four flours, PPWF3 contained a good amount of fiber 8.16%, crude protein 3.18%, total phenolic content 14.48%, moisture 9.5%, vitamin C 13.64 mg/100 g, and total phenolic compound 14.48 (mg/GAE/g), which are maximum and acceptable range values as compared to the other three composite flours and the control group flour. PPWF3 is used as a partial replacement ratio for wheat flour due to its high phenolic content, vitamin C content, and richness in fibers. This composite flour is used to make bread dough, and two samples, G1 and G2, are made, out of which G2 offers better nutritional, functional, and sensory evaluations in comparison with refined wheat bread, which is taken as a control group. Thus, such utilization of food waste in bread making can generate value from waste and improve the nutritional attributes of bread, which may improve an individual’s health.

In today's era vehicles are large in number, the tollbooth seems to become bottleneck to pass through the gates due to their manual operations. This process may take few minutes to pass the toll system. In order to decrease this passing time, we decided to automate the process of toll system by doing technology enablement of Internet of Things (IoT). This work would reduce the manual work and hence would make the passing of the vehicles much faster as compared to traditional toll system.Every vehicle is tagged with a RFID tag, which has vehicle's registration number in it, which can be sensed by RFID reader present at tollbooth.RFID reader will send this information to IoT controller (Arduino). Sensed registration number can looked in to cloud database for getting wallet balance and if sufficient balance is there, and then toll charges can be deducted automatically. While rider can enjoy pause free ride and will be intimated about his trip deduction charges. For interaction of client a mobile app will also be designed with which client can track all logs of payments and can add money in wallet using the app. The sole purpose of this paper is to reduce the hardships caused by manual toll collection system and it assures time saving, fuel conservation and contributing in saving of money by making process automatic.