Morphological and Biochemical Responses Associated With Resistance to Earias Vittella Fab. (Lepidoptera: Noctuidae) In Okra Varieties

Response of morphological and biochemical traits against okra shoot and fruit borer in different okra varieties were studied. Among the fifteen okra varieties under test Kashi Satdhari was the most superior variety as it showed lowest (2.60) per cent shoot damage followed by D-1-87-5 (3.62%) and Pusa A-4 (4.24%). On the contrary, in Pusa Sawani highest level of shoot infestation (16.23%) followed by SB-2 (13.74%) as against Kashi Pragati (check) 10.08 per cent were recorded. Further, okra variety Kashi Satdhari (7.87%) showed lowest fruit infestation and was considered as least susceptible variety which was at par with NO-136 (8.77%), D-1-87-5 (9.12%) and Kashi Leela (9.38%). Amongst all the okra varieties evaluated for their susceptibility to fruit infestation, Pusa Sawani and VRO-03 showed relatively higher fruit infestation i.e. 35.17 and 33.41 per cent, respectively and registered as the most inferior varieties against (26.12%) Kashi Pragati (check). The correlation study between fruit infestation and morphological factors implied that primary branching and trichome length adversely affect the borer infestation. Further, phenol and phosphorus availability in host plant also showed negative effect on shoot and fruit borer infestation. Bangladesh J. Bot. 50(4): 1059-1066, 2021 (December)

A ROBUST DEEP LEARNING APPROACH TO ENHANCE THE ACCURACY OF POMEGRANATE FRUIT DISEASE DETECTION UNDER REAL FIELD CONDITION

Pomegranate fruits are infected by various diseases and pests, which negatively affect food security, productivity, and quality. Recent advancements in deep learning with Convolutional Neural Networks (CNNs) have significantly improved the accuracy of fruit disease detection and classification. The main objective of this investigation is to find the most suitable deep-learning architecture to enhance fruit disease detection and classification accuracy. The current study proposed an efficient deep learning-based approach to detect the most prominent diseases of pomegranate such as bacterial blight, anthracnose, fruit spot, wilt, and fruit borer. For experimentation, a total of 1493 stagewise diseases development images of fruits and leaves are captured via a camera of an interval of 25 days for a total of six months duration. Additionally, extensive data augmentation was performed to increase the dataset, data diversity and to achieve a more robust model for disease detection. For this, the performance of three CNN-based architectures i.e., ResNet50, ResNet18, and Inception-V3 on a real field environment dataset was measured. Experimental results revealed that the proposed CNN-based ResNet50 architecture has effectively detected and classified five different types of diseases whose symptoms are not well defined and with the capability to deal with complex backgrounds. The optimized ResNet50 model achieved 97.92 % test accuracy over ResNet18 (87.5 %) and Inception-V3 (78.75 %) on learning rate 0.001. The multiclass cross-entropy loss function is applied for determining the error rate. To deal with CNN ‘Black Box’ problem Grad-CAM model can be used in the future. The proposed method will help the agricultural industry in detecting the most prominent diseases of pomegranate, which are likely to cause a decrease in productivity, thereby avoiding economic loss.

ROLE OF PLANT SECONDARY METABOLITES IN COMBATING PEST INDUCED STRESS IN BRINJAL (SOLANUM MELONGENA L.)

Brinjal or eggplant (Solanum melongena L.) is known as a vegetable of diet because it contains high moisture and low calorific value. It is also a good source of antioxidants and phytonutrients. Brinjal is widely grown in the South and South-East Asian countries and is the second most important vegetable in India. It belongs to the Solanaceae family. Shoot and fruit borer (Leucinodes orbonalis) pest of brinjal is the most widespread one and it has the ability to affect any of the developmental stages of brinjal. Plants and their insect herbivores have had a long and intimate evolutionary association that has resulted in many complex interactions mediated by specialized plant metabolites like phenolics, alkaloids, terpenoids, cyanogenic glycosides etc. Frequent and excessive use of insecticides has become a common practice now which only increases the probability of resistance development and resurgence of pest. Hence to develop an effective approach to combat this pest understanding of its feeding mechanism and chemistry of its interaction with the fruit is necessary. The importance of the secondary metabolites in the field of chemical biology and in pest management is discussed in this study.

Symptoms of Damage to Soybean Varieties Due to Major Pest Attacks in South Sulawesi, Indonesia

South Sulawesi Province is one of the centers for soybean development in Indonesia. The varieties that are widely planted by farmers in South Sulawesi include Anjasmoro, Argomulyo, Grobogan, Gema, Dering-1, and Burangrang. These varieties have different levels of seed yield and damage levels. This paper aims to provide an overview and information about the types of soybean varieties, the level of pest damage, and the types of pests that cause damage to soybean varieties developed by farmers in South Sulawesi Province. The method used is to collect various information in the form of secondary data and primary data from research results related to soybean varieties, types of pests that damage soybean plants and the level of damage caused by soybean pests in South Sulawes. The results obtained provide information that the highest level of leaf damage caused by Spodoptera litura F. occurred in the Anjasmoro variety 10.94–32.69% followed by Argomulyo 10.16–26.17% and Grobogan 8.61–24.81%. The highest level of pod damage due to pod sucking was found in Burangrang varieties, namely 13.20%, Gema 12.51%, Dering 10.5%, Argomulyo 9.40%, Grobogan 8.50%, and Anjasmoro 7.70%. The level of fruit damage caused by the fruit borer Etiella zinckenella T., the highest occurred in Detam-1 15.71%, Ring 14.50%, Burangrang 10.60%, Gema 10.0%, Argomulyo 8.20%, Grobogan 7.10%, and Anjasmoro 6.70%. The rate of soybean yield loss caused by S. litura F. was the highest at Anjasmoro 8.97%–11.29%, then Grobogan 7.88–12.80%, and Argomulyo 6.77–14.90%. Meanwhile, the percentage of seed yield loss caused by the attack of the pest Nezara viridula L. ranged from 10.0–41.0% for all varieties. Likewise with Riptortus linearis F., the percentage of soybean seed loss caused ranged from 15 to 79% for all varieties.

Yield Performance of Non-spiny Brinjal Variety VRM (Br)2 in Northern Zone of Tamil Nadu, India

Aim: To identify the yield performance of non-spiny brinjal variety VRM (Br)2. Study Design: Non-spiny brinjal variety VRM (Br)2 was developed by hybridization between Senur local x spiny brinjal VRM (Br) 1 followed by pedigree method of selection. Place and Duration of the Study: The present study was carried out at northern districts of Tamil Nadu viz., Vellore, Ranipet, Tirupathur, Thiruvannamalai, Dharmapuri and Krishnagiri during 2015-2016. Methodology: VRM (Br) 2 was evaluated under different trials during 2017-2020 at various locations along with ruling check variety VRM (Br) 1.The observations were recorded at yield and yield characters. Results: VRM (Br) 2 recorded highest fruit yield of 46.35 t/ha as compared to check variety VRM (Br) 1 (32.85 t/ha). It was 41.00 % higher fruit yield over check variety VRM (Br) 1 and moderately resistant to major insect pests. viz., epilachna beetle, whiteflies and shoot and fruit borer. Conclusion: All the plant and fruit characters are similar to spiny brinjal VRM (Br) 1, whereas the spines are absent in the variety VRM (Br) 2. Due to it’s non-spiny nature, intercultural operations viz., harvesting, packing, storage and transport are easy to do.

Assessment of Fruit Drop in Different Cultivars of Litchi

Individual panicles produce hundreds of pistillate flowers but only a small proportion of these bear fruit and reach maturity. There are some stages of fruit drop during growth and development caused by different factors. An experimental trial was conducted in National Active Germplasm Site (NAGS) at ICAR-NRC on Litchi, Muzaffarpur, to assess the fruit drop due to different factors during 2014–2015. The result revealed that four factors viz., improper pollination and fertilization, embryo abortion, seed and fruit borer, and normal fruit abscission were associated with fruit drop in litchi and fruit drop varied from 23.53–77.54% with a maximum in Shahi and lowest in Elaichi during the first week of flowering. Fruit drop increased to 92.65–97.86% during the third week of flowering because of improper pollination and fertilization, and it reached a maximum level of 98.51–99.70% at the time of ripening stage with the lowest in Deshi. Embryo abortion was one primary cause of fruit drop during the 4th week whereas infestation of seed and fruit borer was the major factor for fruit drop during the 5–7th week. Such fruit drop can be controlled by managing the infestation of borer. Abscission due to ethylene production and heat stress during the maturity of fruit was yet another cause of fruit drop. This study will help to researcher to find out the time of infestation of seed and fruit borer which causes heavy fruit drop and it can be controlled with pest management option.