The Indian Society of Agricultural Engineers
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Maturity Stage Prediction for Cabbage Harvesting Using Deep Learning Algorithms
No full textAccurate and timely detection of crop maturity is crucial for maximizing the benefits of crop harvesting at optimal time. Estimating crop maturity helps farmers to harvest crop at the optimal time, and to get better quality of the produce. This study investigates the application of deep learning techniques to predict the optimal maturity stage of cabbage for harvesting. The Teachable Machine learning model available on Google was used to detect the maturity of cabbage for harvesting. This algorithm relies on RGB (red, green, and blue) images to classify cabbage into two distinct stages: \u27matured\u27 (class 1) and \u27pre-matured\u27 (class 2). A dataset consisting of 630 RGB images were collected from an experimental field using an RGB camera. The Teachable Machine randomly divided this image dataset into two segments: a training set (85%) and a testing set (15%). In this study, the model was trained and tested for three batch sizes, i.e. 16, 32 and 64, three epochs, i.e., 25, 50 and 75, and three learning rates, i.e., 0.01, 0.005 and 0.001. The combination of 16 batch sizes, 75 epochs and a 0.001 learning rate has given the best classification accuracy (95%) for both classes. The identified deep learning architecture was able to classify cabbages as mature and immature with 94% accuracy. The sensitivity (96%) was found higher than the accuracy, which is a good indicator of the performance of the model architecture. The type-II error of developed architecture was only 0.04, which is a better achievement. However, real-world implementation of the model may face challenges, such as varying lighting and environmental conditions, which could affect the model’s accuracy. Addressing these factors will be essential to ensure reliable performance in diverse agricultural settings. Furthermore, model\u27s use can potentially provide economic benefits to farmers by optimizing harvesting time, which could lead to cost savings and improved yield quality
Oyster Mushroom Farming with IoT and Artificial Intelligence: A Comprehensive Review
No full textOyster mushrooms are rich in protein, minerals, and fiber. They provide a nutritious, cholesterol-free food source with medicinal benefits. Despite their increasing popularity, oyster mushroom production faces challenges to meet current demand. This article explores how the Internet of Things (IoT) and Artificial Intelligence (AI) are revolutionising oyster mushroom cultivation, enhancing yield and quality through precise climate control, automation in management, and predictive analytics. Due to the limitation of suitable articles describing oyster mushroom farming with IoT and AI, only thirty-eight relevant articles were reviewed and analysed for this study. Within the existing literature, various machine learning and deep learning architectures have been deployed in oyster mushroom cultivation, demonstrating proficiency in specific tasks. Temperature and humidity sensors, often integrated with microcontrollers, emerged as the predominant choice for regulating the microclimate within mushroom houses. Through a comprehensive literature review, it is evident that integrating IoT and AI has the potential to boost mushroom yield and quality while reducing labour requirements in mushroom houses
Advancements in Agricultural Engineering: Transforming Indian Agriculture for a Sustainable Future
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Effect of drying and packaging on storability of stevia leaves (Stevia rebaudiana Bertoni)
Get PDFFresh stevia leaves (Stevia rebaudiana Bertoni) were dried by open sun drying, mechanical drying at 50, 60 and 70°C and green house (simple and with north wall reflection) drying methods. The dried samples were packaged in airtight high density polyethene (HDPE) and polyethylene terephthalate (PET) jars and stored at ambient conditions. The effect of drying and packaging on storability were evaluated on the basis of quality parameters such as colour, total soluble solids (TSS), total sugar, ash and moisture content. It was observed from the study that the fresh stevia leaves dried by mechanical drying at 60o for 210 min, packaged in HDPE package can be stored under ambient condition for more than three months as the quality was maintained with moisture content (9.43%); total sugar (8.88%); ash content (6.76%); total soluble solids (9.79%) and overall acceptability (78±2.49%). Packaging material, storage period, storage condition and drying method had a significant effect on the different quality parameters and shelf life of stevia leaves
Seaweeds – a potential source of food, feed and fertiliser
Get PDFMany communities in the world consume naturally growing and cultivated/ farmed seaweeds as food. Currently, commercial cultivation/farming produces more than 96% of seaweeds in the world and only around 3-4% is obtained from wild harvest (noncultivated). Naturally occurring and cultivated seaweeds are categorized into green, brown and red seaweeds, based on their pigmentation. More than 200 species of seaweeds are of commercial value, but only around 10 species of seaweeds are popularly cultivated. China, Indonesia, the Republic of Korea and the Philippines are the leading producers of cultured/ farmed species (viz. Eucheuma, Japanese kelp, Gracilaria, Unndaria pinnatifid); and Chile, China and Norway for wild species (mainly brown and red) and Chilean kelp. Seaweeds are rich in dietary fiber (polysaccharides), essential amino acids, major and micronutrients (minerals), vitamins etc. It has been reported that some species are a good source of plant growth regulators. Mainly, the people in China, Japan and Korea relish the soups, stews, flakes, coatings, snacks, etc., made from seaweeds. The use of seaweeds as human food in India is not very common. The second major use of seaweeds after food is the extraction of three important hydrocolloids (Agar, Alginate and Carrageenan). These are used as food additives and in many other industrial applications. A small portion of seaweed is used as an ingredient as livestock feed and fish feed. Seaweed meal and liquid extract of seaweed have been tried in conjunction with inorganic fertilizers with beneficial effects on crop yield, quality produce and soil health. Seaweed is an important marine resource and the coastline in India can be utilized to commercially cultivate seaweed species beneficial for human health and plants. Research is required in developing functional foods, health foods and nutraceuticals from seaweeds to improve the health and nutritional status of the human population. This paper briefly describes the status of production and utilization of seaweeds in different parts of the world
Comparative evaluation of actual evapotranspiration of capsicum inside and outside of naturally ventilated polyhouse
Get PDFThis study was conducted to study the relationship between capsicum crop evapotranspiration inside (ETCin) and crop evapotranspiration outside (ETCout) the naturally ventilated polyhouse (NVPH) using meteorological parameters. Polyhouse has a straightway impact on air temperature and relative humidity while it indirectly influences soil temperature and soil moisture inside the structure. Under this study, crop evapotranspiration was estimated by conventional method i.e., obtaining reference evapotranspiration from weather data recorded inside the polyhouse and multiplying it with crop coefficient values of capsicum crop. Reference crop evapotranspiration inside and outside the polyhouse found as 745.19 mm and 590.22 mm, respectively whereas capsicum crop evapotranspiration inside and outside the polyhouse was 868.40 mm and 694.16 mm, respectively. The results of the study revealed that the relationship between weekly ETCin and ETCout can be expressed mathematically as ETCin = 0.84 ETCout. This implies that, there was approximately 15 % lower crop evapotranspiration requirement for the capsicum crop inside the naturally ventilated polyhouse as compared to outside the polyhouse
Modeling of engineering properties for predicting the mass of acid lime fruit (Citrus aurantifolia Swingle)
Get PDFEngineering properties and their relationships with mass for Phule Sharbati acid lime cultivar were investigated. Relationship between physical properties of fruits and its mass will create tremendous change in the packaging industry. The mean values of engineering properties such as minor diameter, intermediate diameter, major diameter, geometric mean diameter, sphericity, aspect ratio, mass, surface area, volume and true density were found to be 42.55 mm, 41.20 mm, 40.41 mm, 0.94, 0.94, 38.17g, 49997 mm2, 33322.8 mm3 and 1 g/cc, respectively. Regression models were used to predict the effect of mass of acid lime and classified into two: 1–Single and multiple variable regressions of acid lime mass and dimensional characteristics and 2- Single variable regression for geometric mean diameter, sphericity, surface area and volume. Results indicated that mass modeling of acid lime based on minor diameter was found most appropriate in the first classification. In the second classification, the power-law model was noticed best on the basis of the geometric mean diameter, surface area and the volume