Paddy Doctor: A Visual Image Dataset for Paddy Disease Classification

One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support tools to enable effective crop protection measures. However, the lack of availability of public datasets with detailed disease information limits the practical implementation of accurate disease detection systems. This paper presents Paddy Doctor, a visual image dataset for identifying paddy diseases. Our dataset contains 13,876 annotated paddy leaf images across ten classes (nine diseases and normal leaf). We benchmarked the Paddy Doctor using a Convolutional Neural Network (CNN) and two transfer learning approaches, VGG16 and MobileNet. The experimental results show that MobileNet achieves the highest classification accuracy of 93.83\%. We release our dataset and reproducible code in the open source for community use

Synthesis of citric acid using novel Aspergillus niveus obtained from agricultural wastes

Fungus belonging to the genus Aspergillus is considered highly important in the production of various types of enzymes and organic acids. Aspergillus species produce organic acids such as citric acid, itaconic acid, and malic acid, which are one of the most important alternate techniques for chemical processes. Citric acid is an important component in the manufacturing process of food and beverages, pharmaceuticals, cosmetics, toiletries, detergents, and other industries. In this work, A.niveus was isolated from the agricultural waste collected in Kotagiri, The Nilgiris, India. Submerged batch fermentation with a range of low-cost substrates, such as wheat flour, corn starch, and sweet potato, was used to successfully synthesize citric acid by the isolated fungus. In addition, production-related factors such as substrate concentration and incubation time were optimized. The maximum yield of citric acid was produced using A. niveus from corn starch at a concentration 7of 120 g/L after 168 hours at pH 3.2. Furthermore, with a degree of extraction of 91.96, citric acid was extracted from fermentation

Study of Different Sources of Organic Manures in Comparison with RDF on Growth and Yield of Greengram [Vigna radiata (L.) Wilczek]

The field experiment was conducted to evaluate the different sources of organic manures in comparison with recommended dose fertilizer (RDF) on greengram at South farm, Karunya Institute of Technology and Science, Coimbatore, India. The field experiment was laid out in Randomized Block Design and replicated thrice during kharif and rabi seasons of 2022. The field study comprised of nine treatments, two organic treatments with sole organic manures at 100% on N equivalent basis (vermicompost, farmyard manure) another three treatments consisted of 25%, 50% and 75% combination of each manure. These two organic manures (vermicompost, farmyard manure) were combined with 50% RDF through inorganic fertilizers (12.5:25:12.5 of N, P2O5 and K2O kg ha-1) as a combination of organic and inorganic treatments and one sole 100% inorganic treatment through inorganic fertilizer (25:50:25 of N, P2O5 and K2O kg ha-1). A control treatment was maintained in the layout to compare the performance of all the treatments. The results showed that among the different combinations of organic and inorganic treatments, 100% recommended dose of fertilizer through inorganic (25:50:25 of N, P2O5 and K2O kg ha-1) (T7) fertilizer significantly enhanced the growth and yield of greengram which was closely followed by vermicompost 100% on N equivalent basis.&nbsp

Genetic manipulation of anti-nutritional factors in major crops for a sustainable diet in future

The consumption of healthy food, in order to strengthen the immune system, is now a major focus of people worldwide and is essential to tackle the emerging pandemic concerns. Moreover, research in this area paves the way for diversification of human diets by incorporating underutilized crops which are highly nutritious and climate-resilient in nature. However, although the consumption of healthy foods increases nutritional uptake, the bioavailability of nutrients and their absorption from foods also play an essential role in curbing malnutrition in developing countries. This has led to a focus on anti-nutrients that interfere with the digestion and absorption of nutrients and proteins from foods. Anti-nutritional factors in crops, such as phytic acid, gossypol, goitrogens, glucosinolates, lectins, oxalic acid, saponins, raffinose, tannins, enzyme inhibitors, alkaloids, β-N-oxalyl amino alanine (BOAA), and hydrogen cyanide (HCN), are synthesized in crop metabolic pathways and are interconnected with other essential growth regulation factors. Hence, breeding with the aim of completely eliminating anti-nutrition factors tends to compromise desirable features such as yield and seed size. However, advanced techniques, such as integrated multi-omics, RNAi, gene editing, and genomics-assisted breeding, aim to breed crops in which negative traits are minimized and to provide new strategies to handle these traits in crop improvement programs. There is also a need to emphasize individual crop-based approaches in upcoming research programs to achieve smart foods with minimum constraints in future. This review focuses on progress in molecular breeding and prospects for additional approaches to improve nutrient bioavailability in major crops.</jats:p